• Stars
    star
    557
  • Rank 79,968 (Top 2 %)
  • Language
  • Created over 5 years ago
  • Updated over 2 years ago

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

πŸ““ Notes and summaries of various ML, Computer Vision & NLP papers.

ML Papers

This repo contains notes and short summaries of some ML related papers I come across, organized by subjects and the summaries are in the form of PDFs.

Self-Supervised & Contrastive Learning

  • Self-Supervised Relational Reasoning for Representation Learning (2020): [Paper] [Notes]
  • Big Self-Supervised Models are Strong Semi-Supervised Learners (2020) [Paper] [Notes]
  • Debiased Contrastive Learning (2020) [Paper] [Notes]
  • Selfie: Self-supervised Pretraining for Image Embedding (2019): [Paper] [Notes]
  • Self-Supervised Representation Learning by Rotation Feature Decoupling (2019): [Paper] [Notes]
  • Revisiting Self-Supervised Visual Representation Learning (2019): [Paper] [Notes]
  • AET vs. AED: Unsupervised Representation Learning by Auto-Encoding Transformations (2019): [Paper] [Notes]
  • Boosting Self-Supervised Learning via Knowledge Transfer (2018): [Paper] [Notes]
  • Self-Supervised Feature Learning by Learning to Spot Artifacts (2018): [Paper] [Notes]
  • Unsupervised Representation Learning by Predicting Image Rotations (2018): [Paper] [Notes]
  • Cross Pixel Optical-Flow Similarity for Self-Supervised Learning (2018): [Paper] [Notes]
  • Multi-task Self-Supervised Visual Learning (2017): [Paper] [Notes]
  • Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction (2017): [Paper] [Notes]
  • Colorization as a Proxy Task for Visual Understanding (2017): [Paper] [Notes]
  • Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles (2017): [Paper] [Notes]
  • Unsupervised Visual Representation Learning by Context Prediction (2016): [Paper] [Notes]
  • Colorful image colorization (2016): [Paper] [Notes]
  • Learning visual groups from co-occurrences in space and time (2015): [Paper] [Notes]
  • Discriminative unsupervised feature learning with exemplar convolutional neural networks (2015): [Paper] [Notes]

Semi-Supervised Learning

  • Negative sampling in semi-supervised learning (2020): [Paper] [Notes]
  • Time-Consistent Self-Supervision for Semi-Supervised Learning (2020): [Paper] [Notes]
  • Dual Student: Breaking the Limits of the Teacher in Semi-supervised Learning (2019): [Paper] [Notes]
  • S4L: Self-Supervised Semi-Supervised Learning (2019): [Paper] [Notes]
  • Semi-Supervised Learning by Augmented Distribution Alignment (2019): [Paper] [Notes]
  • MixMatch: A Holistic Approach toSemi-Supervised Learning (2019): [Paper] [Notes]
  • Unsupervised Data Augmentation (2019): [Paper] [Notes]
  • Interpolation Consistency Training for Semi-Supervised Learning (2019): [Paper] [Notes]
  • Deep Co-Training for Semi-Supervised Image Recognition (2018): [Paper] [Notes]
  • Unifying semi-supervised and robust learning by mixup (2019): [Paper] [Notes]
  • Realistic Evaluation of Deep Semi-Supervised Learning Algorithms (2018): [Paper] [Notes]
  • Semi-Supervised Sequence Modeling with Cross-View Training (2018): [Paper] [Notes]
  • Virtual Adversarial Training (2017): [Paper] [Notes]
  • Mean teachers are better role models (2017): [Paper] [Notes]
  • Temporal Ensembling for Semi-Supervised Learning (2017): [Paper] [Notes]
  • Semi-Supervised Learning with Ladder Networks (2015): [Paper] [Notes]

Video Understanding

  • Multiscale Vision Transformers (2021): [Paper] [Notes]
  • ViViT A Video Vision Transformer (2021): [Paper] [Notes]
  • Space-time Mixing Attention for Video Transformer (2021): [Paper] [Notes]
  • Is Space-Time Attention All You Need for Video Understanding (2021): [Paper] [Notes]
  • An Image is Worth 16x16 Words What is a Video Worth (2021): [Paper] [Notes]
  • Temporal Query Networks for Fine-grained Video Understanding (2021): [Paper] [Notes]
  • X3D Expanding Architectures for Efficient Video Recognition (2020): [Paper] [Notes]
  • Temporal Pyramid Network for Action Recognition (2020): [Paper] [Notes]
  • STM SpatioTemporal and Motion Encoding for Action Recognition (2019): [Paper] [Notes]
  • Video Classification with Channel-Separated Convolutional Networks (2019): [Paper] [Notes]
  • Video Modeling with Correlation Networks (2019): [Paper] [Notes]
  • Videos as Space-Time Region Graphs (2018): [Paper] [Notes]
  • SlowFast Networks for Video Recognition (2018): [Paper] [Notes]
  • TSM Temporal Shift Module for Efficient Video Understanding (2018): [Paper] [Notes]
  • Timeception for Complex Action Recognition (2018): [Paper] [Notes]
  • Non-local Neural Networks (2017): [Paper] [Notes]
  • Temporal Segment Networks for Action Recognition in Videos. (2017): [Paper] [Notes]
  • Quo Vadis Action Recognition A New Model and the Kinetics Dataset (2017): [Paper] [Notes]
  • A Closer Look at Spatiotemporal Convolutions for Action Recognition (2017): [Paper] [Notes]
  • ActionVLAD Learning spatio-temporal aggregation for action classification (2017): [Paper] [Notes]
  • Spatiotemporal Residual Networks for Video Action Recognition (2016): [Paper] [Notes]
  • Deep Temporal Linear Encoding Networks (2016): [Paper] [Notes]
  • Temporal Convolutional Networks for Action Segmentation and Detection (2016): [Paper] [Notes]
  • Learning Spatiotemporal Features with 3D Convolutional Network (2014): [Paper] [Notes]

Domain Adaptation, Domain & Out-of-Distribution Generalization

  • Rethinking Distributional Matching Based Domain Adaptation (2020): [Paper] [Notes]
  • Transferability vs. Discriminability: Batch Spectral Penalization (2019): [Paper] [Notes]
  • On Learning Invariant Representations for Domain Adaptation (2019): [Paper] [Notes]
  • Universal Domain Adaptation (2019): [Paper] [Notes]
  • Transferable Adversarial Training (2019): [Paper] [Notes]
  • Multi-Adversarial Domain Adaptation (2018): [Paper] [Notes]
  • Conditional Adversarial Domain Adaptation (2018): [Paper] [Notes]
  • Learning Adversarially Fair and Transferable Representations (2018): [Paper] [Notes]
  • What is the Effect of Importance Weighting in Deep Learning? (2018): [Paper] [Notes]

Explainability

  • Towards Interpreting and Mitigating Shortcut Learning Behavior of NLU Models (2021): [Paper] [Notes]
  • Transformer Interpretability Beyond Attention Visualization (2020): [Paper] [Notes]
  • What shapes feature representations Exploring datasets architectures and training (2020): [Paper] [Notes]
  • Attention-based Dropout Layer for Weakly Supervised Object Localization (2019): [Paper] [Notes]
  • Attention is not Explanation (2019): [Paper] [Notes]
  • SmoothGrad removing noise by adding noise (2017): [Paper] [Notes]
  • Axiomatic Attribution for Deep Networks (2017): [Paper] [Notes]
  • Attention Branch Network: Learning of Attention Mechanism for Visual Explanation (2019): [Paper] [Notes]
  • Paying More Attention to Attention: Improving the Performance of CNNs via Attention Transfer (2016): [Paper] [Notes]

Natural Language Processing (NLP)

  • Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing (2021): [Paper] [Notes]
  • Unsupervised Data Augmentation with Naive Augmentation and without Unlabeled Data (2020): [Paper] [Notes]
  • Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning (2021): [Paper] [Notes]
  • BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning (2020): [Paper] [Notes]
  • FreeLB: Enhanced Adversarial Training for Natural Language Understanding (2020): [Paper] [Notes]
  • MixText: Linguistically-Informed Interpolation for Semi-Supervised Text Classification (2020): [Paper] [Notes]

Generative Modeling

  • Generative Pretraining from Pixels (2020): [Paper] [Notes]
  • Consistency Regularization for Generative Adversarial Networks (2020): [Paper] [Notes]

Unsupervised Learning

  • Invariant Information Clustering for Unsupervised Image Classification and Segmentation (2019): [Paper] [Notes]
  • Deep Clustering for Unsupervised Learning of Visual Feature (2018): [Paper] [Notes]

Semantic Segmentation

  • DeepLabv3+: Encoder-Decoder with Atrous Separable Convolution (2018): [Paper] [Notes]
  • Large Kernel Matter, Improve Semantic Segmentation by Global Convolutional Network (2017): [Paper] [Notes]
  • Understanding Convolution for Semantic Segmentation (2018): [Paper] [Notes]
  • Rethinking Atrous Convolution for Semantic Image Segmentation (2017): [Paper] [Notes]
  • RefineNet: Multi-path refinement networks for high-resolution semantic segmentation (2017): [Paper] [Notes]
  • Pyramid Scene Parsing Network (2017): [Paper] [Notes]
  • SegNet: A Deep ConvolutionalEncoder-Decoder Architecture for ImageSegmentation (2016): [Paper] [Notes]
  • ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation (2016): [Paper] [Notes]
  • Attention to Scale: Scale-aware Semantic Image Segmentation (2016): [Paper] [Notes]
  • Deeplab: semantic image segmentation with DCNN, atrous convs and CRFs (2016): [Paper] [Notes]
  • U-Net: Convolutional Networks for Biomedical Image Segmentation (2015): [Paper] [Notes]
  • Fully Convolutional Networks for Semantic Segmentation (2015): [Paper] [Notes]
  • Hypercolumns for object segmentation and fine-grained localization (2015): [Paper] [Notes]

Weakly- and Semi-supervised Semantic segmentation

  • Box-driven Class-wise Region Masking and Filling Rate Guided Loss (2019): [Paper] [Notes]
  • FickleNet: Weakly and Semi-supervised Semantic Segmentation using Stochastic Inference (2019): [Paper] [Notes]
  • Weakly-Supervised Semantic Segmentation Network with Deep Seeded Region Growing (2018): [Paper] [Notes]
  • Learning Pixel-level Semantic Affinity with Image-level Supervision (2018): [Paper] [Notes]
  • Object Region Mining with Adversarial Erasing (2018): [Paper] [Notes]
  • Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Segmentation (2018): [Paper] [Notes]
  • Tell Me Where to Look: Guided Attention Inference Network (2018): [Paper] [Notes]
  • Semi Supervised Semantic Segmentation Using Generative Adversarial Network (2017): [Paper] [Notes]
  • Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation (2015): [Paper] [Notes]
  • Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation (2015): [Paper] [Notes]

Information Retrieval

  • VSE++: Improving Visual-Semantic Embeddings with Hard Negatives (2018): [Paper] [Notes]

Graph Neural Network

  • Pixels to Graphs by Associative Embedding (2017): [Paper] [Notes]
  • Associative Embedding: End-to-End Learning forJoint Detection and Grouping (2017): [Paper] [Notes]
  • Interaction Networks for Learning about Objects , Relations and Physics (2016): [Paper] [Notes]
  • DeepWalk: Online Learning of Social Representation (2014): [Paper] [Notes]
  • The graph neural network model (2009): [Paper] [Notes]

Regularization

  • Manifold Mixup: Better Representations by Interpolating Hidden States (2018): [Paper] [Notes]

Deep learning Methods & Models

Document analysis and segmentation

  • dhSegment: A generic deep-learning approach for document segmentation (2018): [Paper] [Notes]
  • Learning to extract semantic structure from documents using multimodal fully convolutional neural networks (2017): [Paper] [Notes]
  • Page Segmentation for Historical Handwritten Document Images Using Conditional Random Fields (2016): [Paper] [Notes]
  • ICDAR 2015 competition on text line detection in historical documents (2015): [Paper] [Notes]
  • Handwritten text line segmentation using Fully Convolutional Network (2017): [Paper] [Notes]
  • Deep Neural Networks for Large Vocabulary Handwritten Text Recognition (2015): [Paper] [Notes]
  • Page Segmentation of Historical Document Images with Convolutional Autoencoders (2015): [Paper] [Notes]