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

arXiv: https://arxiv.org/abs/2302.14516 · HTML: https://mlsanity.com/paper/2302.14516/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 26 references that are in the map.
- First posted on arXiv 28 Feb 2023.

## Builds on (most cited first, 14 of 14)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020)
- [Full Resolution Image Compression with Recurrent Neural Networks](https://mlsanity.com/paper/1608.05148/) (2016)
- [FloLPIPS: A Bespoke Video Quality Metric for Frame Interpoation](https://mlsanity.com/paper/2207.08119/) (2022)
- [Neural Video Compression using GANs for Detail Synthesis and Propagation](https://mlsanity.com/paper/2107.12038/) (2021)
- [CVEGAN: A Perceptually-inspired GAN for Compressed Video Enhancement](https://mlsanity.com/paper/2011.09190/) (2020)
- [PEA265: Perceptual Assessment of Video Compression Artifacts](https://mlsanity.com/paper/1903.00473/) (2019)
- [Video Compression with CNN-based Post Processing](https://mlsanity.com/paper/2009.07583/) (2020)
- [Perceptually Optimizing Deep Image Compression](https://mlsanity.com/paper/2007.02711/) (2020)

## Built on by (most cited first, 2 of 2)

- [Plug-and-Play Versatile Compressed Video Enhancement](https://mlsanity.com/paper/2504.15380/) (2025)
- [ReLaX-VQA: Residual Fragment and Layer Stack Extraction for Enhancing Video Quality Assessment](https://mlsanity.com/paper/2407.11496/) (2024)

## Abstract (arXiv)

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.