Awesome Long-Tailed Learning
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.
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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
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
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
Speed-up and Compression
Noval XML Settings
Theoritical Studies
Text Classification
Others
Label Correlation
Long-tailed Continual Learning
Train/Test Split
XML Seminar
Survey References:
https://arxiv.org/pdf/1901.00248.pdf
http://www.iith.ac.in/~saketha/research/AkshatMTP2018.pdf
http://manikvarma.org/pubs/bengio19.pdf
The Emerging Trends of Multi-Label Learning
XML Datasets link
Extreme Classification Workshops link