Graph lifelong learning: A survey
Graph learning is a popular approach for perfor ming machine learning on graph-structured
data. It has revolutionized the machine learning ability to model graph data to address …
data. It has revolutionized the machine learning ability to model graph data to address …
Deep long-tailed learning: A survey
Deep long-tailed learning, one of the most challenging problems in visual recognition, aims
to train well-performing deep models from a large number of images that follow a long-tailed …
to train well-performing deep models from a large number of images that follow a long-tailed …
Balanced contrastive learning for long-tailed visual recognition
Real-world data typically follow a long-tailed distribution, where a few majority categories
occupy most of the data while most minority categories contain a limited number of samples …
occupy most of the data while most minority categories contain a limited number of samples …
Long-tailed recognition via weight balancing
In the real open world, data tends to follow long-tailed class distributions, motivating the well-
studied long-tailed recognition (LTR) problem. Naive training produces models that are …
studied long-tailed recognition (LTR) problem. Naive training produces models that are …
Long-tailed visual recognition with deep models: A methodological survey and evaluation
In the real world, large-scale datasets for visual recognition typically exhibit a long-tailed
distribution, where only a few classes contain adequate samples but the others have (much) …
distribution, where only a few classes contain adequate samples but the others have (much) …
Federated learning with label distribution skew via logits calibration
Traditional federated optimization methods perform poorly with heterogeneous data (ie,
accuracy reduction), especially for highly skewed data. In this paper, we investigate the label …
accuracy reduction), especially for highly skewed data. In this paper, we investigate the label …
Distribution alignment: A unified framework for long-tail visual recognition
Despite the success of the deep neural networks, it remains challenging to effectively build a
system for long-tail visual recognition tasks. To address this problem, we first investigate the …
system for long-tail visual recognition tasks. To address this problem, we first investigate the …
Contrastive learning based hybrid networks for long-tailed image classification
Learning discriminative image representations plays a vital role in long-tailed image
classification because it can ease the classifier learning in imbalanced cases. Given the …
classification because it can ease the classifier learning in imbalanced cases. Given the …
Crest: A class-rebalancing self-training framework for imbalanced semi-supervised learning
Semi-supervised learning on class-imbalanced data, although a realistic problem, has been
under studied. While existing semi-supervised learning (SSL) methods are known to perform …
under studied. While existing semi-supervised learning (SSL) methods are known to perform …
A survey on long-tailed visual recognition
The heavy reliance on data is one of the major reasons that currently limit the development
of deep learning. Data quality directly dominates the effect of deep learning models, and the …
of deep learning. Data quality directly dominates the effect of deep learning models, and the …