Development of a Multi-Constraint Loss Function for Image Recovery from Pruned Features

Abstract

Existing image recovery methods have demonstrated that original images can be reconstructed from full features. In this work, we consider a more challenging problem of recovering images from pruned features learned by deep neural networks. Our study addresses this issue by introducing a multi-constraint loss function that integrates L2 distance, sixth-power summation, and total variation regularization to enhance reconstruction quality. The loss function enhances image smoothness and fidelity while ensuring that reconstructed images are encoded as vectors closely aligned with the pruned feature.
The proposed loss function enables robust image recovery, preserving key visual features even at high pruning ratios. Additionally, this study investigates the impact of different pruning levels on reconstruction fidelity, highlighting the trade-off between pruning efficiency and recoverability. These findings provide valuable insights into inverse problems in deep learning and image processing, with implications for security risk assessment, efficient model evaluation, and feature redundancy analysis.