Awesome Long-Tailed Learning

Related tags

Deep Learningawesome-long-tailed-learning
Overview

Awesome Long-Tailed Learning Awesome

This repo pays specially attention to the long-tailed distribution, where labels follow a long-tailed or power-law distribution in the training dataset or/and test dataset. Related papers are sumarized, including its application in computer vision, in particular image classification, and extreme multi-label learning (XML), in particular text categorization.

🔆 Updated 2021-09-27

Long-tailed Learning in Computer Vision

Type of Long-Tailed Learning Methods

Type TST IS CBS CLW NC ENS DA
Meaning Two-Stage Training Instance Sampling Class-Balanced Sampling Class-Level Weighting Normalized Classifier Ensemble Data Augmentation

Long-Tailed Learning Workshops

Year Venue Title Remark
2021 CVPR Open World Vision long-tail, open-set, streaming labels
2021 CVPR Learning from Limited and Imperfect Data (L2ID) label noise, SSL, long-tail

Long-Tailed Learning Papers

Year Venue Title Remark
2021 Arxiv LEARNING FROM LONG-TAILED DATA WITH NOISY LABELS
2021 ICCV Self Supervision to Distillation for Long-Tailed Visual Recognition
2021 ICCV Distilling Virtual Examples for Long-tailed Recognition
2021 CVPR Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification
2021 CVPR MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition
2021 CVPR Disentangling Label Distribution for Long-tailed Visual Recognition
2021 CVPR Long-Tailed Multi-Label Visual Recognition by Collaborative Training on Uniform and Re-Balanced Samplings
2021 CVPR Seesaw Loss for Long-Tailed Instance Segmentation
2021 ICLR IS LABEL SMOOTHING TRULY INCOMPATIBLE WITH KNOWLEDGE DISTILLATION: AN EMPIRICAL STUDY
2021 Arxiv Improving Long-Tailed Classification from Instance Level
2021 Arxiv DISTRIBUTION-AWARE SEMANTICS-ORIENTED PSEUDO-LABEL FOR IMBALANCED SEMI-SUPERVISED LEARNING SSL, Code
2021 Arxiv ResLT: Residual Learning for Long-tailed Recognition
2021 Arxiv Improving Long-Tailed Classification from Instance Level
2021 Arxiv Disentangling Sampling and Labeling Bias for Learning in Large-Output Spaces by Google
2021 Arxiv Breadcrumbs: Adversarial Class-Balanced Sampling for Long-tailed Recognition
2021 Arxiv Procrustean Training for Imbalanced Deep Learning
2021 Arxiv Balanced Knowledge Distillation for Long-tailed Learning CBS+IS, Code
2021 Arxiv Class-Balanced Distillation for Long-Tailed Visual Recognition ENS+DA+IS, by Google Research
2021 Arxiv Distributional Robustness Loss for Long-tail Learning TST+CBS
2021 CVPR Improving Calibration for Long-Tailed Recognition DA+TST, Code
2021 CVPR Distribution Alignment: A Unified Framework for Long-tail Visual Recognition TST
2021 CVPR Adversarial Robustness under Long-Tailed Distribution
2021 CVPR CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning by Google, Code, Tensorflow
2021 ICLR HETEROSKEDASTIC AND IMBALANCED DEEP LEARNING WITH ADAPTIVE REGULARIZATION Code
2021 ICLR LONG-TAILED RECOGNITION BY ROUTING DIVERSE DISTRIBUTION-AWARE EXPERTS ENS+NC, Code, by Zi-Wei Liu
2021 ICLR Long-Tail Learning via Logit Adjustment by Google
2021 AAAI Bag of Tricks for Long-Tailed Visual Recognition with Deep Convolutional Neural Networks
2021 Arxiv Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-tailed Classification
2020 Arxiv ELF: An Early-Exiting Framework for Long-Tailed Classification
2020 CVPR Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective
2020 CVPR Equalization Loss for Long-Tailed Object Recognition
2020 CVPR Deep Representation Learning on Long-tailed Data: A Learnable Embedding Augmentation Perspective
2020 ICLR Decoupling representation and classifier for long-tailed recognition Code
2020 NeurIPS Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning Code
2020 NeurIPS Rethinking the Value of Labels for Improving Class-Imbalanced Learning Code
2020 CVPR Bbn: Bilateral-branch network with cumulative learning for long-tailed visual recognition Code
2019 NeurIPS Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss Code
2019 CVPR Large-Scale Long-Tailed Recognition in an Open World Code, bibtex, by CUHK
2018 - iNatrualist. The inaturalist 2018 competition dataset long-tailed dataset
2017 Arxiv The Devil is in the Tails: Fine-grained Classification in the Wild
2017 NeurIPS Learning to model the tail

eXtreme Multi-label Learning for Information Retrieval

Binary Relevance

Year Venue Title Remark
2019 Machine learning Data Scarcity, Robustness and Extreme Multi-label Classification
2019 WSDM Slice: Scalable linear extreme classifiers trained on 100 million labels for related searches
2017 KDD PPDSparse: A Parallel Primal-Dual Sparse Method for Extreme Classification
2017 AISTATS Label Filters for Large Scale Multilabel Classification
2016 WSDM DiSMEC - Distributed Sparse Machines for Extreme Multi-label Classification
2016 ICML PD-Sparse: A Primal and Dual Sparse Approach to Extreme Multiclass and Multilabel Classification

Tree-based Methods

Year Venue Title Remark
2021 KDD Extreme Multi-label Learning for Semantic Matching in Product Search by Amazon, code
2020 arXiv Probabilistic Label Trees for Extreme Multi-label Classification PLT survey, code
2020 arXiv Online probabilistic label trees
2020 AISTATS LdSM: Logarithm-depth Streaming Multi-label Decision Trees Instance tree,c++ code
2019 NeurIPS AttentionXML: Extreme Multi-Label Text Classification with Multi-Label Attention Based Recurrent Neural Networks Label tree
2019 arXiv Bonsai - Diverse and Shallow Trees for Extreme Multi-label Classification Label tree
2018 ICML CRAFTML, an Efficient Clustering-based Random Forest for Extreme Multi-label Learning Instance tree
2018 WWW Parabel: Partitioned Label Trees for Extreme Classification with Application to Dynamic Search Advertising Label tree...by Manik Varma
2016 ICML Extreme F-Measure Maximization using Sparse Probability Estimates Label tree
2016 KDD Extreme Multi-label Loss Functions for Recommendation, Tagging, Ranking & Other Missing Label Applications Instance tree
2014 KDD A Fast, Accurate and Stable Tree-classifier for eXtreme Multi-label Learning Instance tree, python implementation
2013 ICML Label Partitioning For Sublinear Ranking Label tree
2013 WWW Multi-Label Learning with Millions of Labels: Recommending Advertiser Bid Phrases for Web Pages Instance tree, Random Forest, Gini Index
2011 NeurIPS Efficient label tree learning for large scale object recognition Label tree, multi-class
2010 NeurIPS Label embedding trees for large multi-class tasks Label tree, multi-class
2008 ECML Workshop Effective and Efficient Multilabel Classification in Domains with Large Number of Labels Label tree

Embedding-based Methods

Year Venue Title Remark
2019 AAAI Distributional Semantics Meets Multi-Label Learning bibtex
2019 arXiv Ranking-Based Autoencoder for Extreme Multi-label Classification
2019 NeurIPS Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Ouput Spaces by Google Research
2017 KDD AnnexML: Approximate Nearest Neighbor Search for Extreme Multi-label Classification
2015 NeurIPS Sparse Local Embeddings for Extreme Multi-label Classification
2014 ICML Large-scale Multi-label Learning with Missing Labels
2014 ICML Multi-label Classification via Feature-aware Implicit Label Space Encoding
2013 ICML Efficient Multi-label Classification with Many Labels
2012 NeurIIPS Feature-aware Label Space Dimension Reduction for Multi-label Classification
2011 IJCAI WSABIE: Scaling Up To Large Vocabulary Image Annotation bibtex
2009 NeurIPS Multi-Label Prediction via Compressed Sensing
2008 KDD Extracting Shared Subspaces for Multi-label Classification

Speed-up and Compression

Year Venue Title Remark
2020 KDD Large-Scale Training System for 100-Million Classification at Alibaba Applied Data Science Track
2020 arXiv SOLAR: Sparse Orthogonal Learned and Random Embeddings
2020 ICLR EXTREME CLASSIFICATION VIA ADVERSARIAL SOFTMAX APPROXIMATION
2019 AISTATS Stochastic Negative Mining for Learning with Large Output Spaces by Google
2019 NeurIPS Extreme Classification in Log Memory using Count-Min Sketch: A Case Study of Amazon Search with 50M Products Rice University, bibtex
2019 arXiv An Embarrassingly Simple Baseline for eXtreme Multi-label Prediction
2019 arXiv Accelerating Extreme Classification via Adaptive Feature Agglomeration bibtex, authors from IIT
2019 SDM Fast Training for Large-Scale One-versus-All Linear Classifiers using Tree-Structured Initialization code bibtex

Noval XML Settings

Year Venue Title Remark
2020 arXiv Extreme Multi-label Classification from Aggregated Labels by Inderjit Dhillon. This paper considers multi-instance learning in XML
2020 arXiv Unbiased Loss Functions for Extreme Classification With Missing Labels by Rohit Babbar. Missing labels
2020 ICML Deep Streaming Label Learning code, by Dacheng Tao, streaming multi-label learning
2016 arXiv Streaming Label Learning for Modeling Labels on the Fly by Dacheng Tao, streaming multi-label learning

Theoritical Studies

Year Venue Title Remark
2019 ICML Sparse Extreme Multi-label Learning with Oracle Property Code, by Weiwei Liu
2019 NeurIPS Multilabel reductions: what is my loss optimising? bibtex, by Google

Text Classification

Year Venue Title Remark
2021 ICML SiameseXML: Siamese Networks meet Extreme Classifiers with 100M Labels
2020 KDD Correlation Networks for Extreme Multi-label Text Classification code
2020 arXiv GNN-XML: Graph Neural Networks for Extreme Multi-label Text Classification
2020 ICML Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Clusters for Extreme Multi-label Text Classification code
2019 ACL Large-Scale Multi-Label Text Classification on EU Legislation Eur-Lex 4.3K, bibtex
2019 arXiv X-BERT: eXtreme Multi-label Text Classification with BERT code by Yiming Yang, Inderjit Dhillon
2019 NeurIPS AttentionXML: Extreme Multi-Label Text Classification with Multi-Label Attention Based Recurrent Neural Networks
2018 EMNLP Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces few-shot, zero-shot, evaluation metric
2018 NeurIPS A no-regret generalization of hierarchical softmax to extreme multi-label classification code, PLT code
2017 SIGIR Deep Learning for Extreme Multi-label Text Classification by Yiming Yang at CMU, bibtex

Others

Label Correlation

Year Venue Title Remark
2019 ICML DL2: Training and Querying Neural Networks with Logic
2015 KDD Discovering and Exploiting Deterministic Label Relationships in Multi-Label Learning
2010 KDD Multi-Label Learning by Exploiting Label Dependency

Long-tailed Continual Learning

Year Venue Title Remark
2020 ECCV Imbalanced Continual Learning with Partitioning Reservoir Sampling

Train/Test Split

Year Venue Title Remark
2021 Arxiv Stratified Sampling for Extreme Multi-Label Data

XML Seminar

Year Venue Title Remark
2019 Dagstuhl Seminar 18291 Extreme Classification

Survey References:

  1. https://arxiv.org/pdf/1901.00248.pdf
  2. http://www.iith.ac.in/~saketha/research/AkshatMTP2018.pdf
  3. http://manikvarma.org/pubs/bengio19.pdf
  4. The Emerging Trends of Multi-Label Learning

XML Datasets link

Extreme Classification Workshops link

Owner
Stomach_ache
Stomach_ache
GitHub Repository
Official Implementation of CVPR 2022 paper: "Mimicking the Oracle: An Initial Phase Decorrelation Approach for Class Incremental Learning"

(CVPR 2022) Mimicking the Oracle: An Initial Phase Decorrelation Approach for Class Incremental Learning ArXiv This repo contains Official Implementat

Yujun Shi 24 Nov 01, 2022
MNIST, but with Bezier curves instead of pixels

bezier-mnist This is a work-in-progress vector version of the MNIST dataset. Samples Here are some samples from the training set. Note that, while the

Alex Nichol 15 Jan 16, 2022
Torch-mutable-modules - Use in-place and assignment operations on PyTorch module parameters with support for autograd

Torch Mutable Modules Use in-place and assignment operations on PyTorch module p

Kento Nishi 7 Jun 06, 2022
Benchmark datasets, data loaders, and evaluators for graph machine learning

Overview The Open Graph Benchmark (OGB) is a collection of benchmark datasets, data loaders, and evaluators for graph machine learning. Datasets cover

1.5k Jan 05, 2023
Direct Multi-view Multi-person 3D Human Pose Estimation

Implementation of NeurIPS-2021 paper: Direct Multi-view Multi-person 3D Human Pose Estimation [paper] [video-YouTube, video-Bilibili] [slides] This is

Sea AI Lab 251 Dec 30, 2022
High-fidelity 3D Model Compression based on Key Spheres

High-fidelity 3D Model Compression based on Key Spheres This repository contains the implementation of the paper: Yuanzhan Li, Yuqi Liu, Yujie Lu, Siy

5 Oct 11, 2022
Multi-task Learning of Order-Consistent Causal Graphs (NeuRIPs 2021)

Multi-task Learning of Order-Consistent Causal Graphs (NeuRIPs 2021) Authors: Xinshi Chen, Haoran Sun, Caleb Ellington, Eric Xing, Le Song Link to pap

Xinshi Chen 2 Dec 20, 2021
DIVeR: Deterministic Integration for Volume Rendering

DIVeR: Deterministic Integration for Volume Rendering This repo contains the training and evaluation code for DIVeR. Setup python 3.8 pytorch 1.9.0 py

64 Dec 27, 2022
This project uses reinforcement learning on stock market and agent tries to learn trading. The goal is to check if the agent can learn to read tape. The project is dedicated to hero in life great Jesse Livermore.

Reinforcement-trading This project uses Reinforcement learning on stock market and agent tries to learn trading. The goal is to check if the agent can

Deepender Singla 1.4k Dec 22, 2022
Torchreid: Deep learning person re-identification in PyTorch.

Torchreid Torchreid is a library for deep-learning person re-identification, written in PyTorch. It features: multi-GPU training support both image- a

Kaiyang 3.7k Jan 05, 2023
Alphabetical Letter Recognition

DecisionTrees-Image-Classification Alphabetical Letter Recognition In these demo we are using "Decision Trees" Our database is composed by Learning Im

Mohammed Firass 4 Nov 30, 2021
Latte: Cross-framework Python Package for Evaluation of Latent-based Generative Models

Cross-framework Python Package for Evaluation of Latent-based Generative Models Latte Latte (for LATent Tensor Evaluation) is a cross-framework Python

Karn Watcharasupat 30 Sep 08, 2022
Unbalanced Feature Transport for Exemplar-based Image Translation (CVPR 2021)

UNITE and UNITE+ Unbalanced Feature Transport for Exemplar-based Image Translation (CVPR 2021) Unbalanced Intrinsic Feature Transport for Exemplar-bas

Fangneng Zhan 183 Nov 09, 2022
Transfer SemanticKITTI labeles into other dataset/sensor formats.

LiDAR-Transfer Transfer SemanticKITTI labeles into other dataset/sensor formats. Content Convert datasets (NUSCENES, FORD, NCLT) to KITTI format Minim

Photogrammetry & Robotics Bonn 64 Nov 21, 2022
Rust bindings for the C++ api of PyTorch.

tch-rs Rust bindings for the C++ api of PyTorch. The goal of the tch crate is to provide some thin wrappers around the C++ PyTorch api (a.k.a. libtorc

Laurent Mazare 2.3k Dec 30, 2022
Scripts of Machine Learning Algorithms from Scratch. Implementations of machine learning models and algorithms using nothing but NumPy with a focus on accessibility. Aims to cover everything from basic to advance.

Algo-ScriptML Python implementations of some of the fundamental Machine Learning models and algorithms from scratch. The goal of this project is not t

Algo Phantoms 81 Nov 26, 2022
HW3 ― GAN, ACGAN and UDA

HW3 ― GAN, ACGAN and UDA In this assignment, you are given datasets of human face and digit images. You will need to implement the models of both GAN

grassking100 1 Dec 13, 2021
Code accompanying our paper Feature Learning in Infinite-Width Neural Networks

Empirical Experiments in "Feature Learning in Infinite-width Neural Networks" This repo contains code to replicate our experiments (Word2Vec, MAML) in

Edward Hu 37 Dec 14, 2022
Deep learning based hand gesture recognition using LSTM and MediaPipie.

Hand Gesture Recognition Deep learning based hand gesture recognition using LSTM and MediaPipie. Demo video using PingPong Robot Files Pretrained mode

Brad 24 Nov 11, 2022
Code for the USENIX 2017 paper: kAFL: Hardware-Assisted Feedback Fuzzing for OS Kernels

kAFL: Hardware-Assisted Feedback Fuzzing for OS Kernels Blazing fast x86-64 VM kernel fuzzing framework with performant VM reloads for Linux, MacOS an

Chair for Sys­tems Se­cu­ri­ty 541 Nov 27, 2022