Improving Image Classification Accuracy through Complementary Intra-Class and Inter-Class Mixup

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%.