{"id":15642500,"url":"https://github.com/swall0w/papers","last_synced_at":"2026-01-07T22:46:15.810Z","repository":{"id":118927190,"uuid":"104854564","full_name":"Swall0w/papers","owner":"Swall0w","description":"This is a repository for summarizing papers especially related to machine 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Papers\nMemos for papers, which are related to ML, CV and NLP.\n\n# CV\n## Recognition\n* [Wide Residual Networks](./papers/000010.Wide_Residual_Networks.md)\n* [Densely Connected Convolutional Networks](./papers/000015.Densely_Connected_Convolutional_Networks.md)\n* [Deep Pyramidal Residual Networks with Separated Stochastic Depth](./papers/000023.Deep_Pyramidal_Residual_Networks_with_Separated_Stochastic_Depth.md)\n* [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and \u003c0.5MB model size](./papers/000029.SqueezeNet_AlexNet-level_accuracy_with_50x_fewer_parameters_and_0.5MB_model_size.md)\n* [Dual Path Networks](./papers/000030.Dual_Path_Networks.md)\n* [CondenseNet: An Efficient DenseNet using Learned Group Convolutions](./papers/000038.CondenseNet_An_Efficient_DenseNet_using_Learned_Group_Convolutions.md)\n* [Recurrent Models of Visual Attention](./papers/000041.Recurrent_Models_of_Visual_Attention.md)\n\n## Detection (Including Instance Segmentation)\n* [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](./papers/000004.Faster_R-CNN_Towards_Real-Time_Object_Detection_with_Region_Proposal_Networks.md)\n* [SSD: Single Shot MultiBox Detector](./papers/000007.SSD_Single_Shot_MultiBox_Detector.md)\n* [Feature Pyramid Networks for Object Detection](./papers/000008.Feature_Pyramid_Networks_for_Object_Detection.md)\n* [DSSD : Deconvolutional Single Shot Detector](./papers/000009.DSSD_Deconvolutional_Single_Shot_Detector.md)\n* [Speed/accuracy trade-offs for modern convolutional object detectors](./papers/000018.Speed_accuracy_trade-offs_for_modern_convolutional_object_detectors.md)\n* [Focal Loss for Dense Object Detection](./papers/000019.Focal_Loss_for_Dense_Object_Detection.md)\n* [DetNet: A Backbone network for Object Detection](./papers/000046.DetNet_A_Backbone_network_for_Object_Detection.md)\n* [Light-Head R-CNN: In Defense of Two-Stage Object Detector](./papers/000048.Light-Head_R-CNN_In_Defense_of_Two-Stage_Object_Detector.md)\n* [Fully Convolutional Instance-aware Semantic Segmentation](./papers/000049.Fully_Convolutional_Instance-aware_Semantic_Segmentation.md)\n* [Mask R-CNN](./papers/000051.Mask_R-CNN.md)\n* [Fast and accurate object detection in high resolution 4K and 8K video using GPUs](./papers/000056.Fast_and_accurate_object_detection_in_high_resolution_4K_and_8K_video_using_GPUs.md)\n* [Revisiting RCNN: On Awakening the Classification Power of Faster RCNN](papers/000062.Revisiting_RCNN.md)\n\n## Pedestrian detection\n* [Faster R-CNN with Densenet for scale aware pedestrian detection vis-a-vis head negative suppression](papers/000063_Faster_RCNN_with_densenet.md)\n\n## Semantic Segmentation\n* [Fully Convolutional Networks for Semantic Segmentation](./papers/000013.Fully_Convolutional_Networks_for_Semantic_Segmentation.md)\n* [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](./papers/000016.SegNet_A_Deep_Convolutional_Encoder-Decoder_Architecture_for_Image_Segmentation.md)\n* [U-Net: Convolutional Networks for Biomedical Image Segmentation](./papers/000017.U-Net_Convolutional_Networks_for_Biomedical_Image_Segmentation.md)\n\n## Captioning\n* [Self-critical Sequence Training for Image Captioning](./papers/000001.Self-critical_Sequence_Training_for_Image_Captioning.md)\n* [Show and Tell: A Neural Image Caption Generator](./papers/000002.Show_and_Tell_A_Neural_Image_Caption_Generator.md)\n* [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](./papers/000014.Show_Attend_and_Tell:Neural_Image_Caption_Generation_with_Visual_Attention.md)\n* [Deep visual-semantic alignments for generating image descriptions](./papers/000027.Deep_visual-semantic_alignments_for_generating_image_descriptions.md)\n\n## GAN\n* [Generative Adversarial Nets](./papers/000006.Generative_Adversarial_Nets.md)\n* [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](./papers/000011.Unpaired_Image-to-Image_Translation_using_Cycle-Consistent_Adversarial_Networks.md)\n* [Image-to-Image Translation with Conditional Adversarial Networks](./papers/000022.Image_to_Image_Translation_with_Conditional_Adversarial_Networks.md)\n* [cGAN-based Manga Colorization Using a Single Training Image](./papers/000026.cGAN-based_Manga_Colorization_Using_a_Single_Training_Image.md)\n* [Learning from Simulated and Unsupervised Images through Adversarial Training](./papers/000040.Learning_from_Simulated_and_Unsupervised_Images_through_Adversarial_Training.md)\n\n## Robust Reading\n* [TextBoxes++: A Single-Shot Oriented Scene Text Detector](https://github.com/Swall0w/papers/blob/master/papers/000039.TextBoxes%2B%2B_A%20Single-Shot_Oriented_Scene_Text_Detector.md)\n* [Synthetic data generation for Indic handwritten text recognition](./papers/000044.Synthetic_data_generation_for_Indic_handwritten_text_recognition.md)\n* [Reading Scene Text with Attention Convolutional Sequence Modeling](https://github.com/Swall0w/papers/blob/master/papers/000047.%20Reading_Scene_Text_with_Attention_Convolutional_Sequence_Modeling.md)\n\n## Visualization\n* [SmoothGrad: removing noise by adding noise](./papers/000012.SmoothGrad_removing_noise_by_adding_noise.md)\n\n## Video\n### Tracking\n* [Improving Online Multiple Object tracking with Deep Metric Learning](./papers/000057.Improving_Online_Multiple_Object_tracking_with_Deep_Metric_Learning.md)\n* [SIMPLE ONLINE AND REALTIME TRACKING](./papers/000058.SIMPLE_ONLINE_AND_REALTIME_TRACKING.md)\n\n### Detection\n* [Mobile Video Object Detection with Temporally-Aware Feature Maps](./papers/000053.Mobile_Video_Object_with_Temporally-Aware_Feature_Maps.md)\n* [Towards High Performance Video Object Detection for Mobiles](./papers/000052.Towards_High_Performance_Video_Object_Detection_for_Mobiles.md)\n\n### Else\n* [Multiple Frames Matching for Object Discovery in Video](./papers/000003.Multiple_Frame_Matching_for_Object_Discovery_in_Video.md)\n* [Unsupervised Learning of Video Representations using LSTMs](./papers/000032.Unsupervised_Learning_of_Video_Representations_using_LSTMs.md)\n\n* [Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?](./papers/000055.Can_Spatiotemporal_3D_CNNs_Retrace_the_History_of_2D_CNNs_and_ImageNet.md)\n\n* [DeepMark: One-Shot Clothing Detection](papers/000061_DeepMark_One_shot_Clothing_Detection.md)\n\n# 3D\n* [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](./papers/000036.PointNet++_Deep_Hierarchical_Feature_Learning_on_Point_Sets_in_a_Metric_Space.md)\n\n## Else\n* [FlowNet: Learning Optical Flow with Convolutional Networks](./papers/000020.FlowNet_Learning_Optical_Flow_with_Convolutional_Networks.md)\n* [FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks](./papers/000028.FlowNet2.0_Evolution_of_Optical_Flow_Estimation_with_Deep_Networks.md)\n* [Revisiting Unreasonable Effectiveness of Data in Deep Learning Era](./papers/000031.Revisiting_Unreasonable_Effectiveness_of_Data_in_Deep_Learning_Era.md)\n* [Learning to Compose Domain-Specific Transformations for Data Augmentation](https://github.com/Swall0w/papers/blob/master/papers/000037.Learning%20to_Compose_Domain-Specific_Transformations_for_Data_Augmentation.md)\n* [Spatial Transformer Networks](./papers/000043.Spatial_Transformer_Networks.md)\n\n# NLP\n## NMT\n* [Effective Approaches to Attention-based Neural Machine Translation](./papers/000021.Effective_Approaches_to_Attention-based_Neural_Machine_Translation.md)\n* [Neural Machine Translation by Jointly Learning to Align and Translate](./papers/000025.Neural_Machine_Translation_by_Jointly_Learning_to_Align_and_Translate.md)\n* [Sequence to Sequence Learning with Neural Networks](./papers/000033.Sequence_to_Sequence_Learning_with_Neural_Networks.md)\n* [Attention Is All You Need](./papers/000054.Attention_Is_All_You_Need.md)\n\n\n# ML\n* [Positive-Unlabeled Learning with Non-Negative Risk Estimator](./papers/000035.Positive-Unlabeled_Learning_with_Non-Negative_Risk_Estimator.md)\n\n\n# ELSE\n* [Unsupervised Deep Embedding for Clustering Analysis](./papers/000005.Unsupervised_Deep_Embedding_for_Clustering_Analysis.md)\n* [Attention-Based Models for Speech Recognition](./papers/000024.Attention-Based_Models_for_Speech_Recognition.md)\n* [Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex](./papers/000034.Bridging_the_Gaps_Between_Residual_Learning_Recurrent_Neural_Networks_and_Visual_Cortex.md)\n* [What’s your ML Test Score? A rubric for ML production systems](./papers/000042.Whats_your_ML_Test_Score?_A_rubric_for_ML_production_systems.md)\n* [Multimodal Emoji Prediction](./papers/000045.Multimodal_Emoji_Prediction.md)\n* [Born Again Neural Networks](./papers/000050.Born_Again_Neural_Networks.md)\n* [Digital Auditor: A Framework for Matching Duplicate Invoices](papers/000059.Digital_Auditor.md)\n* [Pedestrian Detection: A Benchmark](papers/000060_Pedestrian_Detection_benchmark.md)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fswall0w%2Fpapers","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fswall0w%2Fpapers","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fswall0w%2Fpapers/lists"}