Many data distributions in the real world are hardly uniform. Instead, skewed
and long-tailed distributions of various kinds are commonly observed. This
poses an interesting problem for machine learning, where most algorithms assume
or work well with uniformly distributed data. The problem is further
exacerbated by current state-of-the-art deep learning models requiring large
volumes of training data. As such, learning from imbalanced data remains a
challenging research problem and a problem that must be solved as we move
towards more real-world applications of deep learning. In the context of class
imbalance, state-of-the-art (SOTA) accuracies on standard benchmark datasets
for classification typically fall less than 75%, even for less challenging
datasets such as CIFAR100. Nonetheless, there has been progress in this niche
area of deep learning. To this end, in this survey, we provide a taxonomy of
various methods proposed for addressing the problem of long-tail
classification, focusing on works that happened in the last few years under a
single mathematical framework. We also discuss standard performance metrics,
convergence studies, feature distribution and classifier analysis. We also
provide a quantitative comparison of the performance of different SOTA methods
and conclude the survey by discussing the remaining challenges and future
research direction.

不均匀数据的深度学习中的挑战及方法研究，重点关注长尾分类问题。

深度长尾分类进展综述

A Survey of Deep Long-Tail Classification Advancements

Long-tail data distributions are prevalent in many real-world networks,
including financial transaction networks, e-commerce networks, and
collaboration networks. Despite the success of recent developments, the
existing works mainly focus on debiasing the machine learning models via graph
augmentation or objective reweighting. However, there is limited literature
that provides a theoretical tool to characterize the behaviors of long-tail
categories on graphs and understand the generalization performance in real
scenarios. To bridge this gap, we propose the first generalization bound for
long-tail classification on graphs by formulating the problem in the fashion of
multi-task learning, i.e., each task corresponds to the prediction of one
particular category. Our theoretical results show that the generalization
performance of long-tail classification is dominated by the range of losses
across all tasks and the total number of tasks. Building upon the theoretical
findings, we propose a novel generic framework Tail2Learn to improve the
performance of long-tail categories on graphs. In particular, we start with a
hierarchical task grouping module that allows label-limited classes to benefit
from the relevant information shared by other classes; then, we further design
a balanced contrastive learning module to balance the gradient contributions of
head and tail classes. Finally, extensive experiments on various real-world
datasets demonstrate the effectiveness of Tail2Learn in capturing long-tail
categories on graphs.

本文提出了长尾数据在图分类中的泛化界限，为了解决这个问题，我们引入了多任务学习，并建立了一个通用框架 Tail2Learn，该框架通过分层任务分组模块和平衡对比学习模块，可以提高长尾类别在图上的性能。

在图上表征长尾类别

Characterizing Long-Tail Categories on Graphs

Discriminativeness is a desirable feature of image captions: captions should
describe the characteristic details of input images. However, recent
high-performing captioning models, which are trained with reinforcement
learning (RL), tend to generate overly generic captions despite their high
performance in various other criteria. First, we investigate the cause of the
unexpectedly low discriminativeness and show that RL has a deeply rooted side
effect of limiting the output words to high-frequency words. The limited
vocabulary is a severe bottleneck for discriminativeness as it is difficult for
a model to describe the details beyond its vocabulary. Then, based on this
identification of the bottleneck, we drastically recast discriminative image
captioning as a much simpler task of encouraging low-frequency word generation.
Hinted by long-tail classification and debiasing methods, we propose methods
that easily switch off-the-shelf RL models to discriminativeness-aware models
with only a single-epoch fine-tuning on the part of the parameters. Extensive
experiments demonstrate that our methods significantly enhance the
discriminativeness of off-the-shelf RL models and even outperform previous
discriminativeness-aware methods with much smaller computational costs.
Detailed analysis and human evaluation also verify that our methods boost the
discriminativeness without sacrificing the overall quality of captions.

本文探讨了针对图像的生成式字幕生成模型具有过高泛化性质的问题，分析了词汇量限制造成的字幕不够详细的原因，并提出了一种鼓励低频词汇生成的简单方法，使得模型能够生成更具独创性的字幕。实验表明，这种方法有效提高了模型的字幕生成质量。

通过缓解增强学习的瓶颈，实现切换到判别式图像字幕生成

Switching to Discriminative Image Captioning by Relieving a Bottleneck of Reinforcement Learning

We introduce Retrieval Augmented Classification (RAC), a generic approach to
augmenting standard image classification pipelines with an explicit retrieval
module. RAC consists of a standard base image encoder fused with a parallel
retrieval branch that queries a non-parametric external memory of pre-encoded
images and associated text snippets. We apply RAC to the problem of long-tail
classification and demonstrate a significant improvement over previous
state-of-the-art on Places365-LT and iNaturalist-2018 (14.5% and 6.7%
respectively), despite using only the training datasets themselves as the
external information source. We demonstrate that RAC's retrieval module,
without prompting, learns a high level of accuracy on tail classes. This, in
turn, frees the base encoder to focus on common classes, and improve its
performance thereon. RAC represents an alternative approach to utilizing large,
pretrained models without requiring fine-tuning, as well as a first step
towards more effectively making use of external memory within common computer
vision architectures.

提出了 Retrieval Augmented Classification (RAC) 方法，并应用于解决长尾分类问题，通过显式引入检索模块，使用非参数化外部内存信息显著提高了 Places365-LT 和 iNaturalist-2018 数据集的性能，尤其是在尾部类别上，为计算机视觉体系结构中更有效地利用外部存储器提供了替代方案。