74 citations · 255 across the 16 of their papers we have counts for
16 papers
Boosting Semi-Supervised Learning with Contrastive Complementary Labeling
Qinyi Deng, Yong Guo, Zhibang Yang +2
Semi-supervised learning (SSL) has achieved great success in leveraging a large amount of unlabeled data to learn a promising classifier. A popular approach is pseudo-labeling that…
Downscaled Representation Matters: Improving Image Rescaling with Collaborative Downscaled Images
Bingna Xu, Yong Guo, Luoqian Jiang +2
Deep networks have achieved great success in image rescaling (IR) task that seeks to learn the optimal downscaled representations, i.e., low-resolution (LR) images, to reconstruct…
Text-Aware Dual Routing Network for Visual Question Answering
Luoqian Jiang, Yifan He, Jian Chen
Visual question answering (VQA) is a challenging task to provide an accurate natural language answer given an image and a natural language question about the image. It involves mul…
Pareto-aware Neural Architecture Generation for Diverse Computational Budgets
Yong Guo, Yaofo Chen, Yin Zheng +5
Designing feasible and effective architectures under diverse computational budgets, incurred by different applications/devices, is essential for deploying deep models in real-world…
Improving Fine-tuning of Self-supervised Models with Contrastive Initialization
Haolin Pan, Yong Guo, Qinyi Deng +3
Self-supervised learning (SSL) has achieved remarkable performance in pretraining the models that can be further used in downstream tasks via fine-tuning. However, these self-super…
Towards Lightweight Super-Resolution with Dual Regression Learning
Yong Guo, Mingkui Tan, Zeshuai Deng +5
Deep neural networks have exhibited remarkable performance in image super-resolution (SR) tasks by learning a mapping from low-resolution (LR) images to high-resolution (HR) images…
Content-Aware Convolutional Neural Networks
Yong Guo, Yaofo Chen, Mingkui Tan +3
Convolutional Neural Networks (CNNs) have achieved great success due to the powerful feature learning ability of convolution layers. Specifically, the standard convolution traverse…
Learning Defense Transformers for Counterattacking Adversarial Examples
Jincheng Li, Jiezhang Cao, Yifan Zhang +2
Deep neural networks (DNNs) are vulnerable to adversarial examples with small perturbations. Adversarial defense thus has been an important means which improves the robustness of D…
Pareto-Frontier-aware Neural Architecture Generation for Diverse Budgets
Yong Guo, Yaofo Chen, Yin Zheng +5
Designing feasible and effective architectures under diverse computation budgets incurred by different applications/devices is essential for deploying deep models in practice. Exis…
Towards Accurate and Compact Architectures via Neural Architecture Transformer
Yong Guo, Yin Zheng, Mingkui Tan +5
Designing effective architectures is one of the key factors behind the success of deep neural networks. Existing deep architectures are either manually designed or automatically se…
Conditional Automated Channel Pruning for Deep Neural Networks
Yixin Liu, Yong Guo, Zichang Liu +5
Model compression aims to reduce the redundancy of deep networks to obtain compact models. Recently, channel pruning has become one of the predominant compression methods to deploy…
Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search
Yong Guo, Yaofo Chen, Yin Zheng +4
Neural architecture search (NAS) has become an important approach to automatically find effective architectures. To cover all possible good architectures, we need to search in an e…
Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution
Yong Guo, Jian Chen, Jingdong Wang +5
Deep neural networks have exhibited promising performance in image super-resolution (SR) by learning a nonlinear mapping function from low-resolution (LR) images to high-resolution…
NAT: Neural Architecture Transformer for Accurate and Compact Architectures
Yong Guo, Yin Zheng, Mingkui Tan +4
Designing effective architectures is one of the key factors behind the success of deep neural networks. Existing deep architectures are either manually designed or automatically se…
Deep Learning for Image Super-resolution: A Survey
Zhihao Wang, Jian Chen, Steven C. H. Hoi
Image Super-Resolution (SR) is an important class of image processing techniques to enhance the resolution of images and videos in computer vision. Recent years have witnessed rema…
The Shallow End: Empowering Shallower Deep-Convolutional Networks through Auxiliary Outputs
Yong Guo, Jian Chen, Qing Du +3
Depth is one of the key factors behind the success of convolutional neural networks (CNNs). Since ResNet, we are able to train very deep CNNs as the gradient vanishing issue has be…