NoisyNN: Exploring the Impact of Information Entropy Change in Learning Systems
Abstract: We investigate the impact of entropy change in deep learning systems by noise injection at different levels, including the embedding space and the image. The series of models that employ our methodology are collectively known as Noisy Neural Networks (NoisyNN), with examples such as NoisyViT and NoisyCNN discussed in the paper. Noise is conventionally viewed as a harmful perturbation in various deep learning architectures, such as convolutional neural networks (CNNs) and vision transformers (ViTs), as well as different learning tasks like image classification and transfer learning. However, this work shows noise can be an effective way to change the entropy of the learning system. We demonstrate that specific noise can boost the performance of various deep models under certain conditions. We theoretically prove the enhancement gained from positive noise by reducing the task complexity defined by information entropy and experimentally show the significant performance gain in large image datasets, such as the ImageNet. Herein, we use the information entropy to define the complexity of the task. We categorize the noise into two types, positive noise (PN) and harmful noise (HN), based on whether the noise can help reduce the complexity of the task. Extensive experiments of CNNs and ViTs have shown performance improvements by proactively injecting positive noise, where we have achieved an unprecedented top 1 accuracy of 95$\%$ on ImageNet. Both theoretical analysis and empirical evidence have confirmed that the presence of positive noise can benefit the learning process, while the traditionally perceived harmful noise indeed impairs deep learning models. The different roles of noise offer new explanations for deep models on specific tasks and provide a new paradigm to improve model performance. Moreover, it reminds us that we can influence the performance of learning systems via information entropy change. Code for reproducing NoisyViT on ImageNet is available at \href{https://anonymous.4open.science/r/NoisyViT-ImageNet/README.md}{NoisyViT}.
Submission Length: Regular submission (no more than 12 pages of main content)
Assigned Action Editor: ~Lu_Jiang1
Submission Number: 3950
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