TL;DR
This paper introduces a neural architecture search method to develop tiny perceptual super-resolution models that outperform existing models in quality while being significantly more efficient for on-device deployment.
Contribution
The work presents a novel NAS approach that jointly searches generator and discriminator architectures for small perceptual SR models, enabling efficient on-device super-resolution.
Findings
Outperforms SRGAN and EnhanceNet in perceptual quality metrics
Achieves up to 26.4× memory efficiency and 33.6× compute efficiency
Demonstrates effective search for SR-optimized discriminator architectures
Abstract
Recent works in single-image perceptual super-resolution (SR) have demonstrated unprecedented performance in generating realistic textures by means of deep convolutional networks. However, these convolutional models are excessively large and expensive, hindering their effective deployment to end devices. In this work, we propose a neural architecture search (NAS) approach that integrates NAS and generative adversarial networks (GANs) with recent advances in perceptual SR and pushes the efficiency of small perceptual SR models to facilitate on-device execution. Specifically, we search over the architectures of both the generator and the discriminator sequentially, highlighting the unique challenges and key observations of searching for an SR-optimized discriminator and comparing them with existing discriminator architectures in the literature. Our tiny perceptual SR (TPSR) models…
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Taxonomy
MethodsDropout · Softmax · Max Pooling · Parameterized ReLU · Sigmoid Activation · *Communicated@Fast*How Do I Communicate to Expedia? · Ethereum Customer Service Number +1-833-534-1729 · Residual Block · Dense Connections · Residual Connection
