193 citations · 457 across the 21 of their papers we have counts for
21 papers
Positive Label Is All You Need for Multi-Label Classification
Zhixiang Yuan, Kaixin Zhang, Tao Huang
Multi-label classification (MLC) faces challenges from label noise in training data due to annotating diverse semantic labels for each image. Current methods mainly target identify…
Knowledge Diffusion for Distillation
Tao Huang, Yuan Zhang, Mingkai Zheng +4
The representation gap between teacher and student is an emerging topic in knowledge distillation (KD). To reduce the gap and improve the performance, current methods often resort…
Avatar Knowledge Distillation: Self-ensemble Teacher Paradigm with Uncertainty
Yuan Zhang, Weihua Chen, Yichen Lu +3
Knowledge distillation is an effective paradigm for boosting the performance of pocket-size model, especially when multiple teacher models are available, the student would break th…
Explicit Abnormality Extraction for Unsupervised Motion Artifact Reduction in Magnetic Resonance Imaging
Yusheng Zhou, Hao Li, Jianan Liu +6
Motion artifacts compromise the quality of magnetic resonance imaging (MRI) and pose challenges to achieving diagnostic outcomes and image-guided therapies. In recent years, superv…
Strong Transferable Adversarial Attacks via Ensembled Asymptotically Normal Distribution Learning
Zhengwei Fang, Rui Wang, Tao Huang +1
Strong adversarial examples are crucial for evaluating and enhancing the robustness of deep neural networks. However, the performance of popular attacks is usually sensitive, for i…
LightViT: Towards Light-Weight Convolution-Free Vision Transformers
Tao Huang, Lang Huang, Shan You +3
Vision transformers (ViTs) are usually considered to be less light-weight than convolutional neural networks (CNNs) due to the lack of inductive bias. Recent works thus resort to c…
Masked Distillation with Receptive Tokens
Tao Huang, Yuan Zhang, Shan You +4
Distilling from the feature maps can be fairly effective for dense prediction tasks since both the feature discriminability and localization priors can be well transferred. However…
Knowledge Distillation from A Stronger Teacher
Tao Huang, Shan You, Fei Wang +2
Unlike existing knowledge distillation methods focus on the baseline settings, where the teacher models and training strategies are not that strong and competing as state-of-the-ar…
DyRep: Bootstrapping Training with Dynamic Re-parameterization
Tao Huang, Shan You, Bohan Zhang +4
Structural re-parameterization (Rep) methods achieve noticeable improvements on simple VGG-style networks. Despite the prevalence, current Rep methods simply re-parameterize all op…
Relational Surrogate Loss Learning
Tao Huang, Zekang Li, Hua Lu +6
Evaluation metrics in machine learning are often hardly taken as loss functions, as they could be non-differentiable and non-decomposable, e.g., average precision and F1 score. Thi…
Accelerating Representation Learning with View-Consistent Dynamics in Data-Efficient Reinforcement Learning
Tao Huang, Jiachen Wang, Xiao Chen
Learning informative representations from image-based observations is of fundamental concern in deep Reinforcement Learning (RL). However, data-inefficiency remains a significant b…
GreedyNASv2: Greedier Search with a Greedy Path Filter
Tao Huang, Shan You, Fei Wang +4
Training a good supernet in one-shot NAS methods is difficult since the search space is usually considerably huge (e.g., ). In order to enhance the supernet's evaluation a…
Gradient Boosted Binary Histogram Ensemble for Large-scale Regression
Hanyuan Hang, Tao Huang, Yuchao Cai +2
In this paper, we propose a gradient boosting algorithm for large-scale regression problems called \textit{Gradient Boosted Binary Histogram Ensemble} (GBBHE) based on binary histo…
Prioritized Architecture Sampling with Monto-Carlo Tree Search
Xiu Su, Tao Huang, Yanxi Li +5
One-shot neural architecture search (NAS) methods significantly reduce the search cost by considering the whole search space as one network, which only needs to be trained once. Ho…
Locally Free Weight Sharing for Network Width Search
Xiu Su, Shan You, Tao Huang +4
Searching for network width is an effective way to slim deep neural networks with hardware budgets. With this aim, a one-shot supernet is usually leveraged as a performance evaluat…
Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images
Tao Huang, Songjiang Li, Xu Jia +2
In the last few years, image denoising has benefited a lot from the fast development of neural networks. However, the requirement of large amounts of noisy-clean image pairs for su…
MG-GCN: Fast and Effective Learning with Mix-grained Aggregators for Training Large Graph Convolutional Networks
Tao Huang, Yihan Zhang, Jiajing Wu +2
Graph convolutional networks (GCNs) have been employed as a kind of significant tool on many graph-based applications recently. Inspired by convolutional neural networks (CNNs), GC…
Stretchable Cells Help DARTS Search Better
Tao Huang, Shan You, Yibo Yang +4
Differentiable neural architecture search (DARTS) has gained much success in discovering flexible and diverse cell types. To reduce the evaluation gap, the supernet is expected to…
Data Agnostic Filter Gating for Efficient Deep Networks
Xiu Su, Shan You, Tao Huang +5
To deploy a well-trained CNN model on low-end computation edge devices, it is usually supposed to compress or prune the model under certain computation budget (e.g., FLOPs). Curren…
Quantum circuit architecture search for variational quantum algorithms
Yuxuan Du, Tao Huang, Shan You +2
Variational quantum algorithms (VQAs) are expected to be a path to quantum advantages on noisy intermediate-scale quantum devices. However, both empirical and theoretical results e…
GreedyNAS: Towards Fast One-Shot NAS with Greedy Supernet
Shan You, Tao Huang, Mingmin Yang +3
Training a supernet matters for one-shot neural architecture search (NAS) methods since it serves as a basic performance estimator for different architectures (paths). Current meth…