Deep learning algorithms face great challenges with long-tailed data
distribution which, however, is quite a common case in real-world scenarios.
Previous methods tackle the problem from either the aspect of input space
(re-sampling classes with different frequencies) or loss space (re-weighting
classes with different weights), suffering from heavy over-fitting to tail
classes or hard optimization during training. To alleviate these issues, we
propose a more fundamental perspective for long-tailed recognition, i.e., from
the aspect of parameter space, and aims to preserve specific capacity for
classes with low frequencies. From this perspective, the trivial solution
utilizes different branches for the head, medium, and tail classes
respectively, and then sums their outputs as the final results is not feasible.
Instead, we design the effective residual fusion mechanism -- with one main
branch optimized to recognize images from all classes, another two residual
branches are gradually fused and optimized to enhance images from medium+tail
classes and tail classes respectively. Then the branches are aggregated into
final results by additive shortcuts. We test our method on several benchmarks,
i.e., long-tailed version of CIFAR-10, CIFAR-100, Places, ImageNet, and
iNaturalist 2018. Experimental results manifest the effectiveness of our
method. Our code is available at this https URL

本研究提出通过保留特定类别低频率的能力，并设计了一种有效的残差融合机制来缓解极不平衡数据分布的深度学习算法应用长尾数据分布时普遍存在的问题，通过在参数空间的角度来解决长尾识别问题，并在多个基准数据集上进行了测试，证明了方法的有效性。