Self Distillation via Iterative Constructive Perturbations

TL;DR

This paper introduces a novel cyclic optimization framework using Iterative Constructive Perturbation (ICP) to enhance neural network training by jointly optimizing model parameters and input data, improving performance and generalization.

Contribution

It proposes ICP within a self-distillation framework, enabling iterative input perturbations that refine representations and improve neural network training beyond traditional methods.

Findings

Significant performance improvements across various training scenarios

Effective mitigation of overfitting and generalization gaps

Enhanced intermediate feature representations

Abstract

Deep Neural Networks have achieved remarkable achievements across various domains, however balancing performance and generalization still remains a challenge while training these networks. In this paper, we propose a novel framework that uses a cyclic optimization strategy to concurrently optimize the model and its input data for better training, rethinking the traditional training paradigm. Central to our approach is Iterative Constructive Perturbation (ICP), which leverages the model's loss to iteratively perturb the input, progressively constructing an enhanced representation over some refinement steps. This ICP input is then fed back into the model to produce improved intermediate features, which serve as a target in a self-distillation framework against the original features. By alternately altering the model's parameters to the data and the data to the model, our method effectively addresses the gap between fitting and generalization, leading to enhanced performance. Extensive experiments demonstrate that our approach not only mitigates common performance bottlenecks in neural networks but also demonstrates significant improvements across training variations.