DeepLearning
A collection of research papers, datasets and software on Deep Learning
https://github.com/axruff/DeepLearning
Last synced: 12 days ago
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Analysis and Interpretability
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Logic and Semantics
- 2015 - Visualizing and Understanding Recurrent Networks
- 2016 - Discovering Causal Signals in Images
- 2016 - **[Grad-CAM - grad-cam)
- 2017 - Visualizing the Loss Landscape of Neural Nets
- 2019 - **[SURVEY
- 2018 - GAN Dissection: Visualizing and Understanding Generative Adversarial Networks
- 2018 Interactive tool
- 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
- 2020 - Shortcut Learning in Deep Neural Networks
- 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
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Anomaly Detection
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Instance Segmentation
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Interactive Segmentation
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Models
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3D Shape
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Capsule Networks
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Composition
- 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
- 2019 - Local Relation Networks for Image Recognition
- 2017 - Teaching Compositionality to CNNs
- 2020 - Concept Bottleneck Models
- 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
- 2005 - Image Parsing: Unifying Segmentation, Detection, and Recognition
- 2005 - Image Parsing: Unifying Segmentation, Detection, and Recognition
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Context and Attention
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Logic and Semantics
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Multi-level
- 2014 - **[SPP-Net
- 2016 - **[ParseNet
- 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
- 2017 - Feature Pyramid Networks for Object Detection
- 2018 - **[DeepLabv3
- 2019 - **[FastFCN
- 2019 - Making Convolutional Networks Shift-Invariant Again
- 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
- 2019 - Fixing the train-test resolution discrepancy
- 2016 - **[DeepLab
- 2017 - Rethinking Atrous Convolution for Semantic Image Segmentation
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Transformers
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- 1998 - **[LeNet
- 2013 - Learning Hierarchical Features for Scene Labeling
- 2013 - **[R-CNN
- 2014 - **[Seq2Seq
- 2014 - **[VGG
- 2014 - **[GoogleNet
- 2014 - Neural Turing Machines
- 2015 - **[ResNet
- 2015 - Spatial Transformer Networks
- 2016 - **[WRN - residual-networks)
- 2015 - **[FCN
- 2015 - **[U-net
- 2016 - **[Xception
- Implementation
- 2016 - **[V-Net
- 2017 - **[MobileNets
- Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
- 2018 - **[TernausNet
- 2018 - CubeNet: Equivariance to 3D Rotation and Translation
- 2018 - Deep Rotation Equivariant Network
- 2018 - ArcFace: Additive Angular Margin Loss for Deep Face Recognition
- 2019 - **[PacNet
- 2019 - Decoders Matter for Semantic Segmentation: Data-Dependent Decoding Enables Flexible Feature Aggregation
- 2019 - Panoptic Feature Pyramid Networks
- 2019 - **[DeeperLab
- 2019 - **[EfficientNet
- 2019 - Hamiltonian Neural Networks
- 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 - **[Seq2Seq
- 2012 - **[AlexNet
- 2013 - Learning Hierarchical Features for Scene Labeling
- 2014 - **[OverFeat
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Multitask Learning
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Mutual Learning
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Optical Flow
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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
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Optimization and Regularisation
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Logic and Semantics
- Random search for hyper-parameter optimisation
- **[Adam
- 2017 - The Marginal Value of Adaptive Gradient Methods in Machine Learning
- 2017 - Understanding deep learning requires rethinking generalization
- 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 - Revisiting Small Batch Training for Deep Neural Networks
- 2019 - Cyclical Learning Rates for Training Neural Networks
- 2019 - DeepOBS: A Deep Learning Optimizer Benchmark Suite
- 2019 - A Recipe for Training Neural Networks. Andrey Karpathi Blog
- 2020 - Fantastic Generalization Measures and Where to Find Them
- 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
- Random search for hyper-parameter optimisation
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- **[Dropout
- 2017 - Equilibrium Propagation: Bridging the Gap Between Energy-Based Models and Backpropagation
- 2019 - Training Neural Networks with Local Error Signals - loss) ⭕
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Pruning and Compression
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Logic and Semantics
- 2013 - Do Deep Nets Really Need to be Deep?
- 2015 - Learning both Weights and Connections for Efficient Neural Networks
- 2015 - Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
- 2015 - Distilling the Knowledge in a Neural Network
- 2017 - Learning Efficient Convolutional Networks through Network Slimming - [[github]](https://github.com/liuzhuang13/slimming) ⭕
- 2018 - Rethinking the Value of Network Pruning
- 2018 - Slimmable Neural Networks
- 2019 - Universally Slimmable Networks and Improved Training Techniques
- 2019 - The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
- 2019 - AutoSlim: Towards One-Shot Architecture Search for Channel Numbers
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Reinforcement Learning
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Inverse Reinforcement Learning
- **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
- **[OPENSURFACES
- **[ShapeNet
- ShapeNet: An Information-Rich 3D Model Repository
- [paper
- **[3D ShapeNets
- **[BLEND SWAP
- **[DTD
- **[MegaDepth
- Microsoft **[COCO
- 2020 - **[CARLA
- A Browsable Petascale Reconstruction of the Human Cortex
- 2021 - Medical Segmentation Decathlon. Generalisable 3D Semantic Segmentation
- DAWNBench: is a benchmark suite for end-to-end deep learning training and inference.
- 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)
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Programming Languages
Categories
Reinforcement Learning
313
Models
95
Optimization and Regularisation
21
Unsupervised Learning
16
Analysis and Interpretability
15
Semi Supervised
15
Pruning and Compression
10
Segmentation
6
Weakly Supervised
6
Optical Flow
4
Multitask Learning
3
Semantic Correspondence
3
Mutual Learning
2
Anomaly Detection
2
Instance Segmentation
2
Interactive Segmentation
1
Transfer Learning
1
Sub Categories
Keywords
machine-learning
7
deep-learning
6
pytorch
5
python
3
tensorflow
2
neural-network
2
ai
2
interactive-tools
1
visualizer
1
tensorflow-lite
1
safetensors
1
onnx
1
numpy
1
ml
1
machinelearning
1
keras
1
deeplearning
1
coreml
1
survey
1
reinforcement-learning
1
recommender-system
1
nlp
1
embeddings
1
computer-vision
1
visualization
1
jupyter-notebook
1
interpretability
1
colab
1
transfer-learning
1
synthetic-data
1
style-transfer
1
generative-model
1
domain-adaptation
1
data-science
1
artificial-intelligence
1
resnext
1
resnet
1
pretrained
1
inception
1
imagenet
1
tta
1
test-time-augmentation
1
segmentation
1
pipeline
1
object-detection
1
kaggle
1
jaccard-loss
1
image-segmentation
1
image-processing
1
image-classification
1