Long-tailed(LT) classification is an unavoidable and challenging problem in
the real world. Most of the existing long-tailed classification methods focus
only on solving the inter-class imbalance in which there are more samples in
the head class than in the tail class, while ignoring the intra-lass imbalance
in which the number of samples of the head attribute within the same class is
much larger than the number of samples of the tail attribute. The deviation in
the model is caused by both of these factors, and due to the fact that
attributes are implicit in most datasets and the combination of attributes is
very complex, the intra-class imbalance is more difficult to handle. For this
purpose, we proposed a long-tailed classification framework, known as
\textbf{\textsc{Cognisance}}, which is founded on Coarse-Grained Leading Forest
(CLF) and Multi-Center Loss (MCL), aiming to build a multi-granularity joint
solution model by means of invariant feature learning. In this method, we
designed an unsupervised learning method, i.e., CLF, to better characterize the
distribution of attributes within a class. Depending on the distribution of
attributes, we can flexibly construct sampling strategies suitable for
different environments. In addition, we introduce a new metric learning loss
(MCL), which aims to gradually eliminate confusing attributes during the
feature learning process. More importantly, this approach does not depend on a
specific model structure and can be integrated with existing LT methods as an
independent component. We have conducted extensive experiments and our approach
has state-of-the-art performance in both existing benchmarks ImageNet-GLT and
MSCOCO-GLT, and can improve the performance of existing LT methods. Our codes
are available on GitHub: https://github.com/jinyery/cognisance

通过使用粗粒度引导森林和多中心损失作为不变特征学习的基础，我们提出了一种名为 Cognisance 的长尾分类框架，该框架能够建立多粒度联合解决模型，解决长尾分类中的类内不平衡问题，并且在 ImageNet-GLT 和 MSCOCO-GLT 两个基准上取得了最先进的性能。

基于粗粒度领先森林和多中心损失的长尾分类

Long-Tailed Classification Based on Coarse-Grained Leading Forest and  Multi-Center Loss

Existing long-tailed classification (LT) methods only focus on tackling the
class-wise imbalance that head classes have more samples than tail classes, but
overlook the attribute-wise imbalance. In fact, even if the class is balanced,
samples within each class may still be long-tailed due to the varying
attributes. Note that the latter is fundamentally more ubiquitous and
challenging than the former because attributes are not just implicit for most
datasets, but also combinatorially complex, thus prohibitively expensive to be
balanced. Therefore, we introduce a novel research problem: Generalized
Long-Tailed classification (GLT), to jointly consider both kinds of imbalances.
By "generalized", we mean that a GLT method should naturally solve the
traditional LT, but not vice versa. Not surprisingly, we find that most
class-wise LT methods degenerate in our proposed two benchmarks: ImageNet-GLT
and MSCOCO-GLT. We argue that it is because they over-emphasize the adjustment
of class distribution while neglecting to learn attribute-invariant features.
To this end, we propose an Invariant Feature Learning (IFL) method as the first
strong baseline for GLT. IFL first discovers environments with divergent
intra-class distributions from the imperfect predictions and then learns
invariant features across them. Promisingly, as an improved feature backbone,
IFL boosts all the LT line-up: one/two-stage re-balance, augmentation, and
ensemble. Codes and benchmarks are available on Github:
this https URL

提出了一个新的研究问题：综合考虑类别和属性两个方面的不平衡问题，介绍了一种关于属性无关特征学习的强基线方法（IFL），并发现很多现有的类别不平衡方法在对抗属性级别上不如此方法表现的好。

面向广义长尾分类的不变特征学习

Invariant Feature Learning for Generalized Long-Tailed Classification

We analyze in this paper a random feature map based on a theory of invariance
I-theory introduced recently. More specifically, a group invariant signal
signature is obtained through cumulative distributions of group transformed
random projections. Our analysis bridges invariant feature learning with kernel
methods, as we show that this feature map defines an expected Haar integration
kernel that is invariant to the specified group action. We show how this
non-linear random feature map approximates this group invariant kernel
uniformly on a set of $N$ points. Moreover, we show that it defines a function
space that is dense in the equivalent Invariant Reproducing Kernel Hilbert
Space. Finally, we quantify error rates of the convergence of the empirical
risk minimization, as well as the reduction in the sample complexity of a
learning algorithm using such an invariant representation for signal
classification, in a classical supervised learning setting.

本文研究基于最近引入的不变理论 I 理论的随机特征映射。我们通过组变换的累积分布得到了一组不变的信号标记。本文将不变特征学习与内核方法相结合，并表明该特征映射定义了一个预期的 Haar 积分内核，对指定的组操作具有不变性。此外，本文还分析了此非线性随机特征映射如何在 N 个点上均匀近似组不变内核，并证明了它在等价的不变再生核希尔伯特空间中定义了一个密集的函数空间。最后，我们量化了在经典监督学习环境中使用这种不变信号表示进行信号分类的经验风险最小化的收敛误差率以及样本复杂度的降低。