Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

He Zou, Meng'en Qin, Yu Song, Xiaohui Yang

TL;DR

IB-AdCSCNet addresses the challenge of preserving task-relevant information while discarding redundancy by integrating the information bottleneck objective into a convolutional sparse coding framework with adaptive $\lambda$ learned inside the FISTA iterations. The approach replaces standard convolutional layers with Adaptive Convolutional Sparse Coding (IB-AdCSC) layers, enabling a multi-layer IB trade-off to be learned end-to-end and allowing test-time $\lambda$ adaptation for robustness. The authors provide theoretical grounding connecting mutual information objectives to sparse coding, and demonstrate competitive accuracy with residual networks on CIFAR-10/100 while achieving notable improvements under corrupted data. This work advances interpretable, robust feature learning by marrying information bottleneck principles with sparse representation in deep networks.

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 $λ$ 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.

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

TL;DR

IB-AdCSCNet addresses the challenge of preserving task-relevant information while discarding redundancy by integrating the information bottleneck objective into a convolutional sparse coding framework with adaptive learned inside the FISTA iterations. The approach replaces standard convolutional layers with Adaptive Convolutional Sparse Coding (IB-AdCSC) layers, enabling a multi-layer IB trade-off to be learned end-to-end and allowing test-time adaptation for robustness. The authors provide theoretical grounding connecting mutual information objectives to sparse coding, and demonstrate competitive accuracy with residual networks on CIFAR-10/100 while achieving notable improvements under corrupted data. This work advances interpretable, robust feature learning by marrying information bottleneck principles with sparse representation in deep networks.

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 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.
Paper Structure (22 sections, 9 equations, 4 figures, 6 tables, 1 algorithm)

This paper contains 22 sections, 9 equations, 4 figures, 6 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Paper Contributions.
  3. Related Work
  4. Adaptive Convolutional Sparse Coding Network
  5. Adaptive Convolutional Sparse Coding Layer (IB-AdCSC)
  6. Convolutional sparse coding layer.
  7. $\lambda$ Adaptive learning in FISTA .
  8. Loss Function and Network Training
  9. Loss function.
  10. Robust Algorithm for Adaptive Convolutional Sparse Coding
  11. Experiments
  12. Datasets.
  13. Network architecture.
  14. Network training.
  15. Model Classification Performance
  16. ...and 7 more sections

Figures (4)

  • Figure 1: Schematic of IB-AdCSCNet representation learning. The length of the data and representation represents the amount of data. IB-AdCSCNet gradually reduces the task-related information of input data and eliminates irrelevant information through multi-layer IB trade-off.
  • Figure 2: Schematic of a task performed by an IB-AdCSC layer in IB-AdCSCNet. Through the adaptive IB trade-off, the task goal performed by the IB-AdCSC layer is to retain the relevant information to the greatest extent and eliminate the redundant information. Under the superposition of IB-AdCSC layers, the model can well complete the IB trade-off
  • Figure 3: Accuracy and $\lambda$ under five levels noise. The decreasing polyline corresponds to the accuracy on the vertical axis of the left coordinate, the increasing polyline corresponds to the value of $\lambda$ on the right, and the horizontal axis is the five levels of noise
  • Figure 4: $\lambda$ convergence process: As the iteration proceeds, $\lambda$ increases rapidly after the empirical risk reaches a low level. Readers can watch on https://drive.google.com/file/d/1XMTp-nxQBZQYP-aVWUffA5FEph1Io8hD/view?usp=sharing complete convergence process.