- [2024/03] Our PU-MLC was accepted to ICME 2024 as oral.
- [2024/03] We released ActGen & LocalMamba & EfficientVMamba at arXiv.
- [2024/02] One paper (FreeKD) was accepted to CVPR 2024.
- [2023/09] One paper (DiffKD) was accepted to NeurIPS 2023.
- [2023/07] One paper (AKD) was accepted to ACM MM 2023.
- [2023/05] We released DiffKD and AKD at arXiv.
- [2023/01] One paper (MasKD) was accepted to ICLR 2023.
- [2022/09] One paper (DIST) was accepted to NeurIPS 2022.
- [2022/07] We released LightViT [code] at arXiv.
- [2022/05] We released DIST [code] and MasKD [code] at arXiv.
- [2022/04] One paper (QAS) was accepted to Nature Partner Journals Quantum Information (NPJ QI).
- [2022/03] Two papers (GreedyNASv2 & DyRep) were accepted to CVPR 2022.
- [2022/01] One paper (ReLoss) was accepted to ICLR 2022.
-
[2021/12] We released
MMRazor - a model compression toolkit for model slimming and AutoML, which includes 3 mainstream technologies NAS, pruning, and KD. MMRazor can be easily applied to various projects (e.g., MMDet and MMCls) in OpenMMLab. -
[2021/11] We released GreedyNASv2 at
arXiv. -
[2021/03] One paper about NAS was accepted to CVPR 2021. The NAS benchmark in our paper was released at
github. - [2021/01] One paper about channel number search (pruning) was accepted to ICLR 2021 as spotlight.
-
[2020/11] One paper about NAS was released at
arXiv. Our TopoNAS explicitly learns the topology for differentiable NAS (DARTS), and enjoys significant efficiency improvement on obtained architectures. -
[2020/10] One paper about quantum architecture search (QAS) was released at
arXiv. Our QAS implicitly learns a rule that can well suppress the influence of quantum noise and the barren plateau. - [2020/02] One paper about NAS was accepted to CVPR 2020.
Biography
My name is Tao Huang (黄涛). I am a tenure-track assistant professor in John Hopcropt Center for Computer Science, School of Computer Science, Shanghai Jiao Tong University since 2025. I received my Ph.D. degree in Computer Science at The University of Sydney, advised by Prof. Chang Xu.
Before that, I obtained the Bachelor's degree at Huazhong University of Science and Technology, and then spent two years as a Researcher at SenseTime.
My major research interests lie within efficient deep learning algorithms and their applications in computer vision tasks:
- Efficient architectures: NAS, handcrafts ...
- Efficient training: training acceleration, KD, PEFT ...
- Efficient data: self-supervised learning, generated training contents ...
- Efficient inference: pruning, dynamic networks ...
- Efficient large models: VLMs, Generative Models, VLAs ...
Tao Huang
Assistant Professor, PhD Advisor
Shanghai Jiao Tong University
Email: t.huang[at]sjtu.edu.cn
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