Neural architecture search (NAS) has attracted increasing attention. In recent years,
individual search methods have been replaced by weight-sharing search methods for higher …

Generative adversarial networks (GANs), a novel framework for training generative models
in an adversarial setup, have attracted significant attention in recent years. The two …

Deep learning (DL) techniques have obtained remarkable achievements on various tasks,
such as image recognition, object detection, and language modeling. However, building a …

In the past decade, advances in deep learning have resulted in breakthroughs in a variety of
areas, including computer vision, natural language understanding, speech recognition, and …

Neural Architecture Search (NAS) has been explosively studied to automate the discovery of
top-performer neural networks. Current works require heavy training of supernet or intensive …

This paper proposes a reliable neural network pruning algorithm by setting up a scientific
control. Existing pruning methods have developed various hypotheses to approximate the …

Abstract Conditional Generative Adversarial Networks (cGANs) have enabled controllable
image synthesis for many computer vision and graphics applications. However, recent …

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 …

HardWare-aware Neural Architecture Search (HW-NAS) has recently gained tremendous
attention by automating the design of DNNs deployed in more resource-constrained daily …

This paper studies the single image super-resolution problem using adder neural networks
(AdderNets). Compared with convolutional neural networks, AdderNets utilize additions to …