A comprehensive survey on hardware-aware neural architecture search
Neural Architecture Search (NAS) methods have been growing in popularity. These
techniques have been fundamental to automate and speed up the time consuming and error …
techniques have been fundamental to automate and speed up the time consuming and error …
Weight-sharing neural architecture search: A battle to shrink the optimization gap
Neural architecture search (NAS) has attracted increasing attention. In recent years,
individual search methods have been replaced by weight-sharing search methods for higher …
individual search methods have been replaced by weight-sharing search methods for higher …
Edge-oriented convolution block for real-time super resolution on mobile devices
Efficient and light-weight super resolution (SR) is highly demanded in practical applications.
However, most of the existing studies focusing on reducing the number of model parameters …
However, most of the existing studies focusing on reducing the number of model parameters …
Fourier space losses for efficient perceptual image super-resolution
Many super-resolution (SR) models are optimized for high performance only and therefore
lack efficiency due to large model complexity. As large models are often not practical in real …
lack efficiency due to large model complexity. As large models are often not practical in real …
Contextual transformation network for lightweight remote-sensing image super-resolution
Current super-resolution networks typically reduce network parameters and multiadds
operations by designing lightweight structures, but lightening the convolution layer is often …
operations by designing lightweight structures, but lightening the convolution layer is often …
Practical single-image super-resolution using look-up table
A number of super-resolution (SR) algorithms from in terpolation to deep neural networks
(DNN) have emerged to restore or create missing details of the input low-resolution image …
(DNN) have emerged to restore or create missing details of the input low-resolution image …
Hitchhiker's guide to super-resolution: Introduction and recent advances
With the advent of Deep Learning (DL), Super-Resolution (SR) has also become a thriving
research area. However, despite promising results, the field still faces challenges that …
research area. However, despite promising results, the field still faces challenges that …
Nas-bench-asr: Reproducible neural architecture search for speech recognition
Powered by innovations in novel architecture design, noise tolerance techniques and
increasing model capacity, Automatic Speech Recognition (ASR) has made giant strides in …
increasing model capacity, Automatic Speech Recognition (ASR) has made giant strides in …
Repsr: Training efficient vgg-style super-resolution networks with structural re-parameterization and batch normalization
This paper explores training efficient VGG-style super-resolution (SR) networks with the
structural re-parameterization technique. The general pipeline of re-parameterization is to …
structural re-parameterization technique. The general pipeline of re-parameterization is to …
Learning series-parallel lookup tables for efficient image super-resolution
Lookup table (LUT) has shown its efficacy in low-level vision tasks due to the valuable
characteristics of low computational cost and hardware independence. However, recent …
characteristics of low computational cost and hardware independence. However, recent …