Non-Exemplar Class-Incremental Learning by Random Auxiliary Classes Augmentation and Mixed Features

Non-exemplar class-incremental learning refers to continual classifying of new and old classes without storing samples of old classes. Since only new class samples are available, catastrophic forgetting of old knowledge often occurs. In this paper, we propose an effective non-exemplar method called RAMF consisting of Random Auxiliary classes augmentation and Mixed Features. On the one hand, we design a novel random auxiliary classes augmentation method, where one augmentation is randomly selected from three augmentations and applied to inputs to generate augmented samples and extra class labels. By extending the data and label space, the model can learn more diverse and transferable representations, which can prevent the model from being biased towards learning task-specific features and facilitate the transfer among different tasks. In a word, when learning new tasks, the random auxiliary class augmentation will reduce the change of feature space and improve model generalization. On the other hand, we propose to replace the new features with mixed features for model optimization since only using new features will largely affect the previous representation embedded in the old feature space. Instead, by mixing new and old features, the cosine similarity is improved by reducing the angle between the current and old features, which allows for better stability over long-term incremental learning without increasing the computational complexity. We have conducted extensive experiments on three benchmarks CIFAR-100, TinyImageNet and ImageNet-Subset, where our method outperforms the state-of-the-art non-exemplar methods and is comparable to high-performance replay-based methods.