MixUp and its variants, such as Manifold MixUp, have two key limitations in
image classification tasks. First, they often neglect mixing within the same
class (intra-class mixup), leading to an underutilization of the relationships
among samples within the same class. Second, although these methods effectively
enhance inter-class separability by mixing between different classes
(inter-class mixup), they fall short in improving intra-class cohesion through
their mixing operations, limiting their classification performance. To tackle
these issues, we propose a novel mixup method and a comprehensive integrated
solution.Our mixup approach specifically targets intra-class mixup, an aspect
commonly overlooked, to strengthen intra-class cohesion-a feature not provided
by current mixup techniques.For each mini-batch, our method utilizes feature
representations of unaugmented original images from each class within the
mini-batch to generate a single synthesized feature representation through
random linear interpolation. All synthesized representations for this
mini-batch are then fed into the classification and loss layers to calculate an
average classification loss that can markedly enhance intra-class cohesion.
Moreover, our integrated solution seamlessly combines our intra-class mixup
method with an existing mixup approach such as MixUp or Manifold MixUp. This
comprehensive solution incorporates inter- and intra-class mixup in a balanced
manner while concurrently improving intra-class cohesion and inter-class
separability. Experimental results on six public datasets demonstrate that our
integrated solution achieves a 0.1% to 3.43% higher accuracy than the best of
either MixUp or our intra-class mixup method, averaging a 1.16% gain. It also
outperforms the better performer of either Manifold MixUp or our intra-class
mixup method by 0.12% to 5.16%, with an average gain of 1.11%.

提出一种综合的混合方法和综合解决方案，针对图像分类任务中的类内混合、类间可分离性和类内内聚性进行增强，实验结果表明，该方法相比于现有方法以及类似方法在分类准确性上具有显著提升。