# Learnt Deep Hyperparameter selection in Adversarial Training for compressed video enhancement with perceptual critic

## TL;DR

This paper introduces a method for selecting perceptually relevant layers in deep networks to improve adversarial training for compressed video enhancement, leading to significant performance gains.

## Contribution

It proposes a neuroscience-inspired layer selection technique as a hyperparameter in the critic network, enhancing perceptual quality in video enhancement.

## Key findings

- Up to 10% FID improvement
- Up to 15% KID improvement
- Effective layer selection based on neuroscience insights

## Abstract

Image based Deep Feature Quality Metrics (DFQMs) have been shown to better correlate with subjective perceptual scores over traditional metrics. The fundamental focus of these DFQMs is to exploit internal representations from a large scale classification network as the metric feature space. Previously, no attention has been given to the problem of identifying which layers are most perceptually relevant. In this paper we present a new method for selecting perceptually relevant layers from such a network, based on a neuroscience interpretation of layer behaviour. The selected layers are treated as a hyperparameter to the critic network in a W-GAN. The critic uses the output from these layers in the preliminary stages to extract perceptual information. A video enhancement network is trained adversarially with this critic. Our results show that the introduction of these selected features into the critic yields up to 10% (FID) and 15% (KID) performance increase against other critic networks that do not exploit the idea of optimised feature selection.