One of the ultimate goals of representation learning is to achieve
compactness within a class and well-separability between classes. Many
outstanding metric-based and prototype-based methods following the
Expectation-Maximization paradigm, have been proposed for this objective.
However, they inevitably introduce biases into the learning process,
particularly with long-tail distributed training data. In this paper, we reveal
that the class prototype is not necessarily to be derived from training
features and propose a novel perspective to use pre-defined class anchors
serving as feature centroid to unidirectionally guide feature learning.
However, the pre-defined anchors may have a large semantic distance from the
pixel features, which prevents them from being directly applied. To address
this issue and generate feature centroid independent from feature learning, a
simple yet effective Semantic Anchor Regularization (SAR) is proposed. SAR
ensures the interclass separability of semantic anchors in the semantic space
by employing a classifier-aware auxiliary cross-entropy loss during training
via disentanglement learning. By pulling the learned features to these semantic
anchors, several advantages can be attained: 1) the intra-class compactness and
naturally inter-class separability, 2) induced bias or errors from feature
learning can be avoided, and 3) robustness to the long-tailed problem. The
proposed SAR can be used in a plug-and-play manner in the existing models.
Extensive experiments demonstrate that the SAR performs better than previous
sophisticated prototype-based methods. The implementation is available at
this https URL

基于语义锚点的正则化方法（SAR）可以通过使用预定义的类锚点来引导特征学习，从而提高紧凑度和类间可分性，避免引入偏差和错误。该方法在现有模型中可以以即插即用方式使用，并在大规模实验中优于之前的方法。