Inverse Reinforcement Learning

• **[ImJoy
• **[mlflow
• 2021 - Medical Segmentation Decathlon. Generalisable 3D Semantic Segmentation
• DAWNBench: is a benchmark suite for end-to-end deep learning training and inference.
• **[Rapid
• **[BLEND SWAP
• **[ShapeNet
• Photo-realistic single image super-resolution using a generative adversarial network (2016)
• **[OPENSURFACES
• Microsoft **[COCO
• 2018 - Free-Form Image Inpainting with Gated Convolution
• 2017 - Generative Image Inpainting with Contextual Attention
• 2020 - RANSAC-Flow: generic two-stage image alignment
• 2020 - **[CARLA
• **PySyft**: A generic framework for privacy preserving deep learning
• 2020 - Fourier Neural Operator for Parametric Partial Differential Equations
• 2017 - Revisiting Unreasonable Effectiveness of Data in Deep Learning Era
• **[DTD
• OpenAI Microscope
• 2018 - Deep Learning: A Critical Appraisal
• 2020 - State of AI Report 2020
• A Closed-form Solution to Photorealistic Image Stylization (2018)
• **[Snorkel
• 2017 - Learning Fashion Compatibility with Bidirectional LSTMs
• 2020 - A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research
• 2019 - High-performance medicine: the convergence of human and artificial intelligence
• **Tune**: A Research Platform for Distributed Model Selection and Training (2018) - project/ray/tree/master/python/ray/tune)
• Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification (2018)
• GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks (2018)
• 2019 - On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inference
• 2015 - Natural Language Object Retrieval
• 2019 - CLEVR-Ref+: Diagnosing Visual Reasoning with Referring Expressions
• ShapeNet: An Information-Rich 3D Model Repository
• [paper
• **[3D ShapeNets
• **[MegaDepth
• A Browsable Petascale Reconstruction of the Human Cortex
• DAWNBench: An End-to-End Deep Learning Benchmark and Competition (paper) (2017)
• BubGAN: Bubble Generative Adversarial Networks for Synthesizing Realistic Bubbly Flow Images
• Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping (2018)
• Deep learning with domain adaptation for accelerated projection‐reconstruction MR (2017)
• Abdominal multi-organ segmentation with organ-attention networks and statistical fusion (2018)
• Prior-aware Neural Network for Partially-Supervised Multi-Organ Segmentation (2019)
• Breast Tumor Segmentation and Shape Classification in Mammograms using Generative Adversarial and Convolutional Neural Network (2018)
• 2019 - H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes
• 2020 - **[TorchIO
• 2020 - Reconstructing lost BOLD signal in individual participants using deep machine learning
• 2019 - Deep learning optoacoustic tomography with sparse data
• 2019 - A deep learning reconstruction framework for X-ray computed tomography with incomplete data
• 2020 - Deep Learning Techniques for Inverse Problems in Imaging
• 2020 - **[Review
• 2020 - Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.
• 2020 - End-To-End Convolutional Neural Network for 3D Reconstruction of Knee Bones from Bi-planar X-Ray Images
• 2020 - Differentiated Backprojection Domain Deep Learning for Conebeam Artifact Removal
• 2020 - Extreme Sparse X-ray Computed Laminography Via Convolutional Neural Networks
• 2021 - **[SliceGAN
• 2021 - DeepPhase: Learning phase contrast signal from dual energy X-ray absorption images
• 2022 - Machine learning denoising of high-resolution X-ray nano­tomography data
• 2014 - Do Convnets Learn Correspondence?
• 2016 - Universal Correspondence Network
• 2016 - Learning Dense Correspondence via 3D-guided Cycle Consistency
• 2017 - Convolutional neural network architecture for geometric matching - rocco/cnngeometric_pytorch)
• 2018 - **[DGC-Net - Net)
• 2018 - An Unsupervised Learning Model for Deformable Medical Image Registration
• 2018 - VoxelMorph: A Learning Framework for Deformable Medical Image Registration
• 2019 - A Deep Learning Framework for Unsupervised Affine and Deformable Image Registration
• 2019 - Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces
• Video-to-Video Synthesis (2018)
• 2017 - PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume - pwc)
• 2020 - Softmax Splatting for Video Frame Interpolation - splatting)
• 2017 - **[TOFlow
• 2020 - Automating turbulence modelling by multi-agent reinforcement learning
• 2017 - "Zero-Shot" Super-Resolution using Deep Internal Learning
• 2018 - Residual Dense Network for Image Restoration
• 2018 - Image Super-Resolution Using Very Deep Residual Channel Attention Networks
• 2019 - Noise2Self: Blind Denoising by Self-Supervision
• 2020 - Improving Blind Spot Denoising for Microscopy
• 2021 - Denoising-based Image Compression for Connectomics
• 2018 - Image Inpainting for Irregular Holes Using Partial Convolutions - Keras)
• 2017 - Globally and Locally Consistent Image Completion
• 2021 - COIN: COmpression with Implicit Neural representations
• **[pix2code
• Fast Interactive Object Annotation with Curve-GCN (2019)
• **Caffe**: Convolutional Architecture for Fast Feature Embedding
• **Crypten**: A framework for Privacy Preserving Machine Learning
• **[DALI
• **[PhotonAI
• **[DeepImageJ
• **[BioImage.IO
• **[DeepImageTranslator
• **[OpenMMLab
• 2016 - An Analysis of Deep Neural Network Models for Practical Applications
• 2020 - Maithra Raghu, Eric Schmidt. A Survey of Deep Learning for Scientific Discovery
• 2021 - Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
• 2016 - Building Machines That Learn and Think Like People
• 2016 - A Berkeley View of Systems Challenges for AI
• 2018 - When Will AI Exceed Human Performance? Evidence from AI Experts
• 2018 - The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation
• 2018 - Deciphering China’s AI Dream: The context, components, capabilities, and consequences of China’s strategy to lead the world in AI
• 2018 - The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities
• 2019 - Deep Nets: What have they ever done for Vision?
• 2020 - The role of artificial intelligence in achieving the Sustainable Development Goals
• 2020 - The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence
• 2021 - Why AI is Harder Than We Think by Melanie Mitchell
• 2020 - Reconstructing lost BOLD signal in individual participants using deep machine learning
• 2019 - Deep learning optoacoustic tomography with sparse data
• 2020 - **[Review
• 2020 - Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.
• 2020 - Automating turbulence modelling by multi-agent reinforcement learning
• 2021 - Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
• 2020 - The role of artificial intelligence in achieving the Sustainable Development Goals
• MLPerf: A broad ML benchmark suite for measuring performance of ML software frameworks, ML hardware accelerators, and ML cloud platforms.
• 2020 - Reconstructing lost BOLD signal in individual participants using deep machine learning
• 2019 - Deep learning optoacoustic tomography with sparse data
• 2020 - **[Review
• 2020 - Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.
• 2020 - Automating turbulence modelling by multi-agent reinforcement learning
• 2021 - Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
• 2020 - The role of artificial intelligence in achieving the Sustainable Development Goals
• 2020 - Reconstructing lost BOLD signal in individual participants using deep machine learning
• 2019 - Deep learning optoacoustic tomography with sparse data
• 2020 - **[Review
• 2020 - Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.
• 2020 - Automating turbulence modelling by multi-agent reinforcement learning
• 2021 - Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
• 2020 - The role of artificial intelligence in achieving the Sustainable Development Goals
• 2020 - Reconstructing lost BOLD signal in individual participants using deep machine learning
• 2019 - Deep learning optoacoustic tomography with sparse data
• 2020 - **[Review
• 2020 - Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.
• 2020 - Automating turbulence modelling by multi-agent reinforcement learning
• 2021 - Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
• 2020 - The role of artificial intelligence in achieving the Sustainable Development Goals
• 2020 - Reconstructing lost BOLD signal in individual participants using deep machine learning
• 2019 - Deep learning optoacoustic tomography with sparse data
• 2020 - **[Review
• 2020 - Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.
• 2020 - Automating turbulence modelling by multi-agent reinforcement learning
• 2021 - Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
• 2020 - The role of artificial intelligence in achieving the Sustainable Development Goals
• 2020 - Reconstructing lost BOLD signal in individual participants using deep machine learning
• 2019 - Deep learning optoacoustic tomography with sparse data
• 2020 - **[Review
• 2020 - Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.
• 2020 - Automating turbulence modelling by multi-agent reinforcement learning
• 2021 - Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
• 2020 - The role of artificial intelligence in achieving the Sustainable Development Goals
• 2020 - Reconstructing lost BOLD signal in individual participants using deep machine learning
• 2019 - Deep learning optoacoustic tomography with sparse data
• 2020 - **[Review
• 2020 - Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.
• 2020 - Automating turbulence modelling by multi-agent reinforcement learning
• 2021 - Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
• 2020 - The role of artificial intelligence in achieving the Sustainable Development Goals
• 2020 - Reconstructing lost BOLD signal in individual participants using deep machine learning
• 2019 - Deep learning optoacoustic tomography with sparse data
• 2020 - **[Review
• 2020 - Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.
• 2020 - Automating turbulence modelling by multi-agent reinforcement learning
• 2021 - Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
• 2020 - The role of artificial intelligence in achieving the Sustainable Development Goals
• 2020 - Reconstructing lost BOLD signal in individual participants using deep machine learning
• 2019 - Deep learning optoacoustic tomography with sparse data
• 2020 - **[Review
• 2020 - Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.
• 2020 - Automating turbulence modelling by multi-agent reinforcement learning
• 2021 - Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
• 2020 - The role of artificial intelligence in achieving the Sustainable Development Goals
• 2020 - Reconstructing lost BOLD signal in individual participants using deep machine learning
• 2019 - Deep learning optoacoustic tomography with sparse data
• 2020 - **[Review
• 2020 - Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.
• 2020 - Automating turbulence modelling by multi-agent reinforcement learning
• 2021 - Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
• 2020 - The role of artificial intelligence in achieving the Sustainable Development Goals
• 2020 - Reconstructing lost BOLD signal in individual participants using deep machine learning
• 2019 - Deep learning optoacoustic tomography with sparse data
• 2020 - **[Review
• 2020 - Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.
• 2020 - Automating turbulence modelling by multi-agent reinforcement learning
• 2021 - Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
• 2020 - The role of artificial intelligence in achieving the Sustainable Development Goals
• 2020 - Reconstructing lost BOLD signal in individual participants using deep machine learning
• 2019 - Deep learning optoacoustic tomography with sparse data
• 2020 - **[Review
• 2020 - Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.
• 2020 - Automating turbulence modelling by multi-agent reinforcement learning
• 2021 - Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
• 2020 - The role of artificial intelligence in achieving the Sustainable Development Goals
• 2020 - Reconstructing lost BOLD signal in individual participants using deep machine learning
• 2019 - Deep learning optoacoustic tomography with sparse data
• 2020 - **[Review
• 2020 - Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.
• 2020 - Automating turbulence modelling by multi-agent reinforcement learning
• 2021 - Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
• 2020 - The role of artificial intelligence in achieving the Sustainable Development Goals
• **Glow**: Compiler for Neural Network hardware accelerators
• **Lucid**: A collection of infrastructure and tools for research in neural network interpretability
• **[ml-surveys [github
• **[Interactive Tools
• **[Ignite
• **[PyTorch Toolbelt
• **[Cadene
• **[PyTorch Lightning
• **[TorchIO
• **[OPENSURFACES
• 2021 - Medical Segmentation Decathlon. Generalisable 3D Semantic Segmentation
• **[Efemarai
• 2020 - Reconstructing lost BOLD signal in individual participants using deep machine learning
• 2019 - Deep learning optoacoustic tomography with sparse data
• 2020 - **[Review
• 2020 - Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.
• 2020 - Automating turbulence modelling by multi-agent reinforcement learning
• 2018 - Image Inpainting for Irregular Holes Using Partial Convolutions - Keras)
• 2017 - Globally and Locally Consistent Image Completion
• 2019 - High-performance medicine: the convergence of human and artificial intelligence
• 2021 - Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
• 2018 - Human-level intelligence or animal-like abilities?
• 2020 - The role of artificial intelligence in achieving the Sustainable Development Goals
• 2019 - High-performance medicine: the convergence of human and artificial intelligence
• 2019 - High-performance medicine: the convergence of human and artificial intelligence
• 2020 - Reconstructing lost BOLD signal in individual participants using deep machine learning
• 2019 - Deep learning optoacoustic tomography with sparse data
• 2020 - **[Review
• 2020 - Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.
• 2020 - Automating turbulence modelling by multi-agent reinforcement learning
• 2021 - Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
• 2020 - The role of artificial intelligence in achieving the Sustainable Development Goals
• 2019 - High-performance medicine: the convergence of human and artificial intelligence
• 2020 - Reconstructing lost BOLD signal in individual participants using deep machine learning
• 2019 - Deep learning optoacoustic tomography with sparse data
• 2020 - **[Review
• 2020 - Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.
• 2020 - Automating turbulence modelling by multi-agent reinforcement learning
• 2021 - BTrack: Bayesian Tracker
• 2019 - High-performance medicine: the convergence of human and artificial intelligence
• 2021 - Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
• 2020 - The role of artificial intelligence in achieving the Sustainable Development Goals
• 2019 - High-performance medicine: the convergence of human and artificial intelligence
• 2020 - Reconstructing lost BOLD signal in individual participants using deep machine learning
• 2019 - Deep learning optoacoustic tomography with sparse data
• 2020 - **[Review
• 2020 - Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.
• 2020 - Automating turbulence modelling by multi-agent reinforcement learning
• 2019 - High-performance medicine: the convergence of human and artificial intelligence
• 2021 - Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
• 2020 - The role of artificial intelligence in achieving the Sustainable Development Goals
• 2020 - Reconstructing lost BOLD signal in individual participants using deep machine learning
• 2019 - Deep learning optoacoustic tomography with sparse data
• 2020 - **[Review
• 2020 - Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.
• 2020 - Automating turbulence modelling by multi-agent reinforcement learning
• 2019 - High-performance medicine: the convergence of human and artificial intelligence
• 2021 - Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
• 2020 - The role of artificial intelligence in achieving the Sustainable Development Goals
• 2019 - High-performance medicine: the convergence of human and artificial intelligence
• 2019 - High-performance medicine: the convergence of human and artificial intelligence
• 2020 - Reconstructing lost BOLD signal in individual participants using deep machine learning
• 2019 - Deep learning optoacoustic tomography with sparse data
• 2020 - **[Review
• 2020 - Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.
• 2020 - Automating turbulence modelling by multi-agent reinforcement learning
• 2021 - Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
• 2020 - The role of artificial intelligence in achieving the Sustainable Development Goals
• 2019 - High-performance medicine: the convergence of human and artificial intelligence
• 2019 - High-performance medicine: the convergence of human and artificial intelligence
• Analytics Zoo (Intel): Distributed TensorFlow, PyTorch, Keras and BigDL on Apache Spark & Ray
• 2020 - Reconstructing lost BOLD signal in individual participants using deep machine learning
• 2019 - Deep learning optoacoustic tomography with sparse data
• 2020 - **[Review
• 2020 - Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.
• 2020 - Automating turbulence modelling by multi-agent reinforcement learning
• 2021 - Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
• 2020 - The role of artificial intelligence in achieving the Sustainable Development Goals
• 2019 - High-performance medicine: the convergence of human and artificial intelligence
• 2020 - Reconstructing lost BOLD signal in individual participants using deep machine learning
• 2019 - Deep learning optoacoustic tomography with sparse data
• 2020 - **[Review
• 2020 - Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.
• 2020 - Automating turbulence modelling by multi-agent reinforcement learning
• 2019 - High-performance medicine: the convergence of human and artificial intelligence
• 2021 - Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
• 2020 - The role of artificial intelligence in achieving the Sustainable Development Goals
• 2019 - High-performance medicine: the convergence of human and artificial intelligence
• Beyond PASCAL: A Benchmark for 3D Object Detection in the Wild [website
• 2020 - Reconstructing lost BOLD signal in individual participants using deep machine learning
• 2019 - Deep learning optoacoustic tomography with sparse data
• 2020 - **[Review
• 2020 - Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.
• 2020 - Automating turbulence modelling by multi-agent reinforcement learning
• 2019 - High-performance medicine: the convergence of human and artificial intelligence
• 2021 - Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
• 2020 - The role of artificial intelligence in achieving the Sustainable Development Goals
• **[ADE20K Dataset
• 2019 - High-performance medicine: the convergence of human and artificial intelligence

• 2014 - **[VGG
• 2014 - **[GoogleNet
• 2015 - **[ResNet
• 2016 - **[WRN - residual-networks)
• 2016 - **[Xception
• 2017 - **[MobileNets
• 2019 - **[EfficientNet
• 2014 - Neural Turing Machines
• 2013 - **[R-CNN
• 2015 - **[FCN
• 2015 - **[U-net
• 2019 - Hamiltonian Neural Networks
• 2015 - Spatial Transformer Networks
• 2018 - **[TernausNet
• 2018 - ArcFace: Additive Angular Margin Loss for Deep Face Recognition
• 1998 - **[LeNet
• 2013 - Learning Hierarchical Features for Scene Labeling
• 2014 - **[Seq2Seq
• Implementation
• 2016 - **[V-Net
• Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
• 2018 - CubeNet: Equivariance to 3D Rotation and Translation
• 2018 - Deep Rotation Equivariant Network
• 2019 - **[PacNet
• 2019 - Decoders Matter for Semantic Segmentation: Data-Dependent Decoding Enables Flexible Feature Aggregation
• 2019 - Panoptic Feature Pyramid Networks
• 2019 - **[DeeperLab
• 2020 - Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysis
• 2020 - Neural Operator: Graph Kernel Network for Partial Differential Equations
• 2021 - Learning Neural Network Subspaces
• 2014 - **[OverFeat
• 2014 - **[Seq2Seq
• 2012 - **[AlexNet
• 2013 - Learning Hierarchical Features for Scene Labeling

Multi-level

• 2019 - Fixing the train-test resolution discrepancy
• 2018 - **[DeepLabv3
• 2016 - **[ParseNet
• 2017 - Feature Pyramid Networks for Object Detection
• 2014 - **[SPP-Net
• 2019 - Making Convolutional Networks Shift-Invariant Again
• 2016 - **[PSPNet
• 2015 - Zoom Better to See Clearer: Human and Object Parsing with Hierarchical Auto-Zoom Net
• 2016 - Attention to Scale: Scale-aware Semantic Image Segmentation
• 2019 - **[FastFCN
• 2019 - **[LEDNet
• 2019 - Feature Pyramid Encoding Network for Real-time Semantic Segmentation
• 2019 - Efficient Segmentation: Learning Downsampling Near Semantic Boundaries
• 2019 - PointRend: Image Segmentation as Rendering
• 2016 - **[DeepLab
• 2017 - Rethinking Atrous Convolution for Semantic Image Segmentation

Transformers

• 2020 - **SURVEY**: A Survey on Visual Transformer
• 2021 - **SURVEY**: Transformers in Vision: A Survey
• 2023 - **SURVEY**: A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks

Context and Attention

• 2016 - Image Captioning with Semantic Attention
• 2018 - **[EncNet - Encoding) ⭕
• 2018 - Tell Me Where to Look: Guided Attention Inference Network

Composition

• 2019 - Local Relation Networks for Image Recognition
• 2020 - Concept Bottleneck Models
• 2005 - Image Parsing: Unifying Segmentation, Detection, and Recognition
• 2013 - Complexity of Representation and Inference in Compositional Models with Part Sharing
• 2017 - Interpretable Convolutional Neural Networks
• 2017 - Teaching Compositionality to CNNs
• 2005 - Image Parsing: Unifying Segmentation, Detection, and Recognition
• 2005 - Image Parsing: Unifying Segmentation, Detection, and Recognition
• 2005 - Image Parsing: Unifying Segmentation, Detection, and Recognition
• 2005 - Image Parsing: Unifying Segmentation, Detection, and Recognition
• 2005 - Image Parsing: Unifying Segmentation, Detection, and Recognition
• 2005 - Image Parsing: Unifying Segmentation, Detection, and Recognition
• 2005 - Image Parsing: Unifying Segmentation, Detection, and Recognition
• 2005 - Image Parsing: Unifying Segmentation, Detection, and Recognition
• 2005 - Image Parsing: Unifying Segmentation, Detection, and Recognition
• 2005 - Image Parsing: Unifying Segmentation, Detection, and Recognition
• 2005 - Image Parsing: Unifying Segmentation, Detection, and Recognition
• 2005 - Image Parsing: Unifying Segmentation, Detection, and Recognition
• 2005 - Image Parsing: Unifying Segmentation, Detection, and Recognition
• 2005 - Image Parsing: Unifying Segmentation, Detection, and Recognition
• 2019 - Local Relation Networks for Image Recognition
• 2005 - Image Parsing: Unifying Segmentation, Detection, and Recognition
• 2005 - Image Parsing: Unifying Segmentation, Detection, and Recognition
• 2005 - Image Parsing: Unifying Segmentation, Detection, and Recognition
• 2005 - Image Parsing: Unifying Segmentation, Detection, and Recognition
• 2005 - Image Parsing: Unifying Segmentation, Detection, and Recognition
• 2005 - Image Parsing: Unifying Segmentation, Detection, and Recognition
• 2005 - Image Parsing: Unifying Segmentation, Detection, and Recognition
• 2005 - Image Parsing: Unifying Segmentation, Detection, and Recognition

Capsule Networks

• 2017 - Dynamic Routing Between Capsules

3D Shape

• 2020 - **[NeRF
• 2020 - **[SURVEY
• 2020 - [BLOG
• 2020 - AutoInt: Automatic Integration for Fast Neural Volume Rendering
• 2020 - A Curvature and Density‐based Generative Representation of Shapes
• 2021 - Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks

Logic and Semantics

• 2019 - Neural Logic Machines

Logic and Semantics

• 2018 - **[AutoAugment
• 2019 - Self-training with **[Noisy Student
• 2018 - Data Augmentation by Pairing Samples for Images Classification
• 2014 - Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks
• 2017 - Population Based Training of Neural Networks
• 2019 - **[UDA - [[github]](https://github.com/google-research/uda) ⭕
• 2019 - **[MixMatch
• 2017 - Random Erasing Data Augmentation - Erasing)
• 2017 - Smart Augmentation - Learning an Optimal Data Augmentation Strategy
• 2018 - **[Survey
• 2018 - Albumentations: fast and flexible image augmentations - [[github]](https://github.com/albu/albumentations) ✅
• 2019 - **[RealMix
• 2019 - Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules
• 2019 - **[AugMix - research/augmix) ✅
• 2020 - Rain rendering for evaluating and improving robustness to bad weather

Logic and Semantics

• 2015 - Distilling the Knowledge in a Neural Network
• 2015 - Learning both Weights and Connections for Efficient Neural Networks
• 2015 - Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
• 2019 - The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
• 2018 - Rethinking the Value of Network Pruning
• 2017 - Learning Efficient Convolutional Networks through Network Slimming - [[github]](https://github.com/liuzhuang13/slimming) ⭕
• 2019 - Universally Slimmable Networks and Improved Training Techniques
• 2013 - Do Deep Nets Really Need to be Deep?
• 2018 - Slimmable Neural Networks
• 2019 - AutoSlim: Towards One-Shot Architecture Search for Channel Numbers

Logic and Semantics

• 2019 - Representation Learning with Contrastive Predictive Coding
• 2015 - Unsupervised Visual Representation Learning by Context Prediction
• 2016 - Colorful Image Colorization
• 2019 - **[MoCo
• 2016 - Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles
• 2020 - **[SimCLR
• 2016 - Context Encoders: Feature Learning by Inpainting
• 2018 - Unsupervised Representation Learning by Predicting Image Rotations
• 2019 - Greedy InfoMax for Biologically Plausible Self-Supervised Representation Learning
• 2019 - Unsupervised Learning via Meta-Learning
• 2019 - **[PIRL
• 2020 - **::SURVEY::** Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey
• 2020 - **[NeurIPS 2020 Workshop
• 2020 - **[BYOL
• 2021 - [POST
• 2021 - Task Fingerprinting for Meta Learning in Biomedical Image Analysis

Logic and Semantics

• 2019 - **[SURVEY
• 2016 - **[Grad-CAM - grad-cam)
• 2017 - Visualizing the Loss Landscape of Neural Nets
• 2020 - Shortcut Learning in Deep Neural Networks
• 2018 Interactive tool
• 2015 - Visualizing and Understanding Recurrent Networks
• 2016 - Discovering Causal Signals in Images
• 2018 - GAN Dissection: Visualizing and Understanding Generative Adversarial Networks
• 2019 - **[Distill
• 2019 - On the Units of GANs
• 2019 - Unmasking Clever Hans Predictors and Assessing What Machines Really Learn
• 2020 - Actionable Attribution Maps for Scientific Machine Learning
• 2021 - VIDEO: CVPR 2021 Workshop. Interpretable Neural Networks for Computer Vision: Clinical Decisions that are Aided, not Automated
• 2021 - VIDEO. CVPR 2021 Workshop. Interpreting Deep Generative Models for Interactive AI Content Creation by Bolei Zhou (CUHK)
• **[Netron

Logic and Semantics

• 2017 - An Overview of Multi-Task Learning in Deep Neural Networks
• 2017 - Multi-task Self-Supervised Visual Learning
• 2016 - Cross-Stitch Networks for Multi-task Learning

Logic and Semantics

• 2019 - SelFlow: Self-Supervised Learning of Optical Flow - [github]](https://github.com/ppliuboy/SelFlow)
• 2021 - AutoFlow: Learning a Better Training Set for Optical Flow
• 2021 - SMURF: Self-Teaching Multi-Frame Unsupervised RAFT with Full-Image Warping
• 2021 - AutoFlow: Learning a Better Training Set for Optical Flow

Logic and Semantics

• **[Adam
• 2019 - Revisiting Small Batch Training for Deep Neural Networks
• 2019 - A Recipe for Training Neural Networks. Andrey Karpathi Blog
• 2020 - Fantastic Generalization Measures and Where to Find Them
• 2017 - Understanding deep learning requires rethinking generalization
• 2017 - Equilibrium Propagation: Bridging the Gap Between Energy-Based Models and Backpropagation
• Random search for hyper-parameter optimisation
• 2017 - The Marginal Value of Adaptive Gradient Methods in Machine Learning
• 2018 - Error Forward-Propagation: Reusing Feedforward Connections to Propagate Errors in Deep Learning
• 2018 - An Empirical Model of Large-Batch Training
• 2018 - A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay
• 2019 - Switchable Normalization for Learning-to-Normalize Deep Representation
• 2019 - Cyclical Learning Rates for Training Neural Networks
• 2019 - DeepOBS: A Deep Learning Optimizer Benchmark Suite
• 2020 - Descending through a Crowded Valley -- Benchmarking Deep Learning Optimizers
• 2020 - Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth
• 2021 - Revisiting ResNets: Improved Training and Scaling Strategies
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• **[Dropout
• Random search for hyper-parameter optimisation
• 2019 - Training Neural Networks with Local Error Signals - loss) ⭕

Logic and Semantics

• 2017 - Deep Mutual Learning
• 2019 - Feature Fusion for Online Mutual Knowledge Distillation

Logic and Semantics

• 2015 - Constrained Convolutional Neural Networks for Weakly Supervised Segmentation
• 2018 - Deep Learning with Mixed Supervision for Brain Tumor Segmentation
• 2019 - Localization with Limited Annotation for Chest X-rays
• 2019 - Doubly Weak Supervision of Deep Learning Models for Head CT
• 2019 - Training Complex Models with Multi-Task Weak Supervision
• 2020 - Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods

Logic and Semantics

• 2019 - YOLACT Real-time Instance Segmentation
• 2019 - Panoptic Segmentation
• 2019 - The Best of Both Modes: Separately Leveraging RGB and Depth for Unseen Object Instance Segmentation
• 2019 - ShapeMask: Learning to Segment Novel Objects by Refining Shape Priors
• 2019 - Learning to Segment via Cut-and-Paste
• 2021 - Boundary IoU: Improving Object-Centric Image Segmentation Evaluation - iou/)

Logic and Semantics

• 2017 - Mask R-CNN
• 2019 - Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidth

Logic and Semantics

• 2020 - Continuous Adaptation for Interactive Object Segmentation by Learning from Corrections

Logic and Semantics

• 2009 - Anomaly Detection: A Survey
• 2017 - Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery

Logic and Semantics

• 2017 - End-to-end weakly-supervised semantic alignment
• 2019 - SFNet: Learning Object-aware Semantic Correspondence - [[github]](https://github.com/cvlab-yonsei/SFNet)
• 2020 - Deep Semantic Matching with Foreground Detection and Cycle-Consistency

Logic and Semantics

• Transfer Learning