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Saliency Assisted Quantization for Neural Networks

Elmira Mousa Rezabeyk, Salar Beigzad, Yasin Hamzavi, Mohsen Bagheritabar, Seyedeh Sogol Mirikhoozani

TL;DR

This paper tackles the inherent black-box nature of these models by providing real-time explanations during the training phase, compelling the model to concentrate on the most distinctive and crucial aspects of the input, and employs established quantization techniques to address resource constraints.

Abstract

Deep learning methods have established a significant place in image classification. While prior research has focused on enhancing final outcomes, the opaque nature of the decision-making process in these models remains a concern for experts. Additionally, the deployment of these methods can be problematic in resource-limited environments. This paper tackles the inherent black-box nature of these models by providing real-time explanations during the training phase, compelling the model to concentrate on the most distinctive and crucial aspects of the input. Furthermore, we employ established quantization techniques to address resource constraints. To assess the effectiveness of our approach, we explore how quantization influences the interpretability and accuracy of Convolutional Neural Networks through a comparative analysis of saliency maps from standard and quantized models. Quantization is implemented during the training phase using the Parameterized Clipping Activation method, with a focus on the MNIST and FashionMNIST benchmark datasets. We evaluated three bit-width configurations (2-bit, 4-bit, and mixed 4/2-bit) to explore the trade-off between efficiency and interpretability, with each configuration designed to highlight varying impacts on saliency map clarity and model accuracy. The results indicate that while quantization is crucial for implementing models on resource-limited devices, it necessitates a trade-off between accuracy and interpretability. Lower bit-widths result in more pronounced reductions in both metrics, highlighting the necessity of meticulous quantization parameter selection in applications where model transparency is paramount. The study underscores the importance of achieving a balance between efficiency and interpretability in the deployment of neural networks.

Saliency Assisted Quantization for Neural Networks

TL;DR

This paper tackles the inherent black-box nature of these models by providing real-time explanations during the training phase, compelling the model to concentrate on the most distinctive and crucial aspects of the input, and employs established quantization techniques to address resource constraints.

Abstract

Deep learning methods have established a significant place in image classification. While prior research has focused on enhancing final outcomes, the opaque nature of the decision-making process in these models remains a concern for experts. Additionally, the deployment of these methods can be problematic in resource-limited environments. This paper tackles the inherent black-box nature of these models by providing real-time explanations during the training phase, compelling the model to concentrate on the most distinctive and crucial aspects of the input. Furthermore, we employ established quantization techniques to address resource constraints. To assess the effectiveness of our approach, we explore how quantization influences the interpretability and accuracy of Convolutional Neural Networks through a comparative analysis of saliency maps from standard and quantized models. Quantization is implemented during the training phase using the Parameterized Clipping Activation method, with a focus on the MNIST and FashionMNIST benchmark datasets. We evaluated three bit-width configurations (2-bit, 4-bit, and mixed 4/2-bit) to explore the trade-off between efficiency and interpretability, with each configuration designed to highlight varying impacts on saliency map clarity and model accuracy. The results indicate that while quantization is crucial for implementing models on resource-limited devices, it necessitates a trade-off between accuracy and interpretability. Lower bit-widths result in more pronounced reductions in both metrics, highlighting the necessity of meticulous quantization parameter selection in applications where model transparency is paramount. The study underscores the importance of achieving a balance between efficiency and interpretability in the deployment of neural networks.

Paper Structure

This paper contains 16 sections, 8 equations, 3 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Works
  3. Saliency Guided Training
  4. Quantization
  5. Floating Point Operations (FLOPs) in Quantization
  6. Methodology
  7. Datasets
  8. Model Architectures
  9. Quantization and PACT Implementation
  10. Saliency Map Generation
  11. Experiments and Results
  12. Accuracy Comparison
  13. Saliency Map Comparison
  14. Model Accuracy Drop
  15. Discussion
  16. ...and 1 more sections

Figures (3)

  • Figure 1: Comparison of Saliency Maps for Quantized Models for MNIST dataset
  • Figure 2: Comparison of KL divergence loss ($\mathcal{D_{KL}}(f_{\theta_i}(X) \| f_{\theta_i}(\widetilde{X}))$) for the models on MNIST dataset
  • Figure 3: Accuracy drop comparison across different models on MNIST dataset with varying levels of quantization. Although the higher quantization bitwidth (4,4) shows the most salient features as it has a deeper steep. Also, (2, 2) quantization starts with lowest accuracy and drops less which shows lower salient feature learning in the process. The model with a higher bitwidth(4,4) shows more accuracy drop, indicating better saliency