IB-AdCSCNet:Adaptive Convolutional Sparse Coding Network Driven by Information Bottleneck

• URL: http://arxiv.org/abs/2405.14192v1
• Date: Thu, 23 May 2024 05:35:57 GMT
• Title: IB-AdCSCNet:Adaptive Convolutional Sparse Coding Network Driven by Information Bottleneck
• Authors: He Zou, Meng'en Qin, Yu Song, Xiaohui Yang,
• Abstract summary: We introduce IB-AdCSCNet, a deep learning model grounded in information bottleneck theory. IB-AdCSCNet seamlessly integrates the information bottleneck trade-off strategy into deep networks. Experimental results on CIFAR-10 and CIFAR-100 datasets demonstrate that IB-AdCSCNet not only matches the performance of deep residual convolutional networks but also outperforms them when handling corrupted data.
• Score: 4.523653503622693
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the realm of neural network models, the perpetual challenge remains in retaining task-relevant information while effectively discarding redundant data during propagation. In this paper, we introduce IB-AdCSCNet, a deep learning model grounded in information bottleneck theory. IB-AdCSCNet seamlessly integrates the information bottleneck trade-off strategy into deep networks by dynamically adjusting the trade-off hyperparameter $\lambda$ through gradient descent, updating it within the FISTA(Fast Iterative Shrinkage-Thresholding Algorithm ) framework. By optimizing the compressive excitation loss function induced by the information bottleneck principle, IB-AdCSCNet achieves an optimal balance between compression and fitting at a global level, approximating the globally optimal representation feature. This information bottleneck trade-off strategy driven by downstream tasks not only helps to learn effective features of the data, but also improves the generalization of the model. This study's contribution lies in presenting a model with consistent performance and offering a fresh perspective on merging deep learning with sparse representation theory, grounded in the information bottleneck concept. Experimental results on CIFAR-10 and CIFAR-100 datasets demonstrate that IB-AdCSCNet not only matches the performance of deep residual convolutional networks but also outperforms them when handling corrupted data. Through the inference of the IB trade-off, the model's robustness is notably enhanced.

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