Boosting Mapping Functionality of Neural Networks via Latent Feature Generation based on Reversible Learning
This method addresses the challenge of enhancing mapping capability in neural networks for visual recognition without needing extra data augmentation, offering a solution for tasks like image classification and face recognition.
The paper tackles the problem of improving neural network mapping functionality in visual recognition tasks by introducing reversible learning to generate and learn latent features for hard samples, achieving state-of-the-art performance on datasets like MNIST, CIFAR-10/100, and EBPC.
This paper addresses a boosting method for mapping functionality of neural networks in visual recognition such as image classification and face recognition. We present reversible learning for generating and learning latent features using the network itself. By generating latent features corresponding to hard samples and applying the generated features in a training stage, reversible learning can improve a mapping functionality without additional data augmentation or handling the bias of dataset. We demonstrate an efficiency of the proposed method on the MNIST,Cifar-10/100, and Extremely Biased and poorly categorized dataset (EBPC dataset). The experimental results show that the proposed method can outperform existing state-of-the-art methods in visual recognition. Extensive analysis shows that our method can efficiently improve the mapping capability of a network.