4 code implementations • CVPR 2022 • Wenshuo Li, Hanting Chen, Jianyuan Guo, Ziyang Zhang, Yunhe Wang
However, due to the simplicity of their structures, the performance highly depends on the local features communication machenism.
3 code implementations • ICCV 2019 • Hanting Chen, Yunhe Wang, Chang Xu, Zhaohui Yang, Chuanjian Liu, Boxin Shi, Chunjing Xu, Chao Xu, Qi Tian
Learning portable neural networks is very essential for computer vision for the purpose that pre-trained heavy deep models can be well applied on edge devices such as mobile phones and micro sensors.
2 code implementations • NeurIPS 2019 • Yixing Xu, Yunhe Wang, Hanting Chen, Kai Han, Chunjing Xu, DaCheng Tao, Chang Xu
In practice, only a small portion of the original training set is required as positive examples and more useful training examples can be obtained from the massive unlabeled data on the cloud through a PU classifier with an attention based multi-scale feature extractor.
1 code implementation • CVPR 2021 • Hanting Chen, Tianyu Guo, Chang Xu, Wenshuo Li, Chunjing Xu, Chao Xu, Yunhe Wang
Experiments on various datasets demonstrate that the student networks learned by the proposed method can achieve comparable performance with those using the original dataset.
2 code implementations • CVPR 2023 • Zhijun Tu, Jie Hu, Hanting Chen, Yunhe Wang
In this paper, we study post-training quantization(PTQ) for image super resolution using only a few unlabeled calibration images.
3 code implementations • 29 May 2023 • Yuchuan Tian, Hanting Chen, Xutao Wang, Zheyuan Bai, Qinghua Zhang, Ruifeng Li, Chao Xu, Yunhe Wang
Recent releases of Large Language Models (LLMs), e. g. ChatGPT, are astonishing at generating human-like texts, but they may impact the authenticity of texts.
1 code implementation • NeurIPS 2023 • Yuchuan Tian, Hanting Chen, Tianyu Guo, Chao Xu, Yunhe Wang
To this end, we propose a Rank-based PruninG (RPG) method to maintain the ranks of sparse weights in an adversarial manner.
7 code implementations • CVPR 2020 • Hanting Chen, Yunhe Wang, Chunjing Xu, Boxin Shi, Chao Xu, Qi Tian, Chang Xu
The widely-used convolutions in deep neural networks are exactly cross-correlation to measure the similarity between input feature and convolution filters, which involves massive multiplications between float values.
4 code implementations • NeurIPS 2023 • Hanting Chen, Yunhe Wang, Jianyuan Guo, DaCheng Tao
In this study, we introduce VanillaNet, a neural network architecture that embraces elegance in design.
6 code implementations • CVPR 2021 • Hanting Chen, Yunhe Wang, Tianyu Guo, Chang Xu, Yiping Deng, Zhenhua Liu, Siwei Ma, Chunjing Xu, Chao Xu, Wen Gao
To maximally excavate the capability of transformer, we present to utilize the well-known ImageNet benchmark for generating a large amount of corrupted image pairs.
Ranked #1 on Single Image Deraining on Rain100L (using extra training data)
2 code implementations • ICCV 2019 • Han Shu, Yunhe Wang, Xu Jia, Kai Han, Hanting Chen, Chunjing Xu, Qi Tian, Chang Xu
Generative adversarial networks (GANs) have been successfully used for considerable computer vision tasks, especially the image-to-image translation.
1 code implementation • 12 Dec 2023 • Mingjian Zhu, Hanting Chen, Mouxiao Huang, Wei Li, Hailin Hu, Jie Hu, Yunhe Wang
The misuse of AI imagery can have harmful societal effects, prompting the creation of detectors to combat issues like the spread of fake news.
1 code implementation • 6 Feb 2024 • Jianyuan Guo, Hanting Chen, Chengcheng Wang, Kai Han, Chang Xu, Yunhe Wang
Recent advancements in large language models have sparked interest in their extraordinary and near-superhuman capabilities, leading researchers to explore methods for evaluating and optimizing these abilities, which is called superalignment.
1 code implementation • 21 Jun 2021 • Xinyang Lin, Hanting Chen, Yixing Xu, Chao Xu, Xiaolin Gui, Yiping Deng, Yunhe Wang
We study the problem of learning from positive and unlabeled (PU) data in the federated setting, where each client only labels a little part of their dataset due to the limitation of resources and time.
4 code implementations • NeurIPS 2021 • Han Shu, Jiahao Wang, Hanting Chen, Lin Li, Yujiu Yang, Yunhe Wang
With the new operation, vision transformers constructed using additions can also provide powerful feature representations.
no code implementations • 17 Dec 2018 • Hanting Chen, Yunhe Wang, Chang Xu, Chao Xu, DaCheng Tao
Experiments on benchmark datasets and well-trained networks suggest that the proposed algorithm is superior to state-of-the-art teacher-student learning methods in terms of computational and storage complexity.
no code implementations • 3 Feb 2020 • Chuanjian Liu, Kai Han, Yunhe Wang, Hanting Chen, Qi Tian, Chunjing Xu
Quantization neural networks (QNNs) are very attractive to the industry because their extremely cheap calculation and storage overhead, but their performance is still worse than that of networks with full-precision parameters.
no code implementations • 7 Mar 2020 • Hanting Chen, Yunhe Wang, Han Shu, Changyuan Wen, Chunjing Xu, Boxin Shi, Chao Xu, Chang Xu
To promote the capability of student generator, we include a student discriminator to measure the distances between real images, and images generated by student and teacher generators.
no code implementations • CVPR 2020 • Yehui Tang, Yunhe Wang, Yixing Xu, Hanting Chen, Chunjing Xu, Boxin Shi, Chao Xu, Qi Tian, Chang Xu
A graph convolutional neural network is introduced to predict the performance of architectures based on the learned representations and their relation modeled by the graph.
no code implementations • CVPR 2020 • Hanting Chen, Yunhe Wang, Han Shu, Yehui Tang, Chunjing Xu, Boxin Shi, Chao Xu, Qi Tian, Chang Xu
This paper studies the compression and acceleration of 3-dimensional convolutional neural networks (3D CNNs).
no code implementations • CVPR 2021 • Dehua Song, Yunhe Wang, Hanting Chen, Chang Xu, Chunjing Xu, DaCheng Tao
To this end, we thoroughly analyze the relationship between an adder operation and the identity mapping and insert shortcuts to enhance the performance of SR models using adder networks.
no code implementations • 23 Dec 2020 • Kai Han, Yunhe Wang, Hanting Chen, Xinghao Chen, Jianyuan Guo, Zhenhua Liu, Yehui Tang, An Xiao, Chunjing Xu, Yixing Xu, Zhaohui Yang, Yiman Zhang, DaCheng Tao
Transformer, first applied to the field of natural language processing, is a type of deep neural network mainly based on the self-attention mechanism.
no code implementations • 25 Jan 2021 • Yunhe Wang, Mingqiang Huang, Kai Han, Hanting Chen, Wei zhang, Chunjing Xu, DaCheng Tao
With a comprehensive comparison on the performance, power consumption, hardware resource consumption and network generalization capability, we conclude the AdderNet is able to surpass all the other competitors including the classical CNN, novel memristor-network, XNOR-Net and the shift-kernel based network, indicating its great potential in future high performance and energy-efficient artificial intelligence applications.
no code implementations • 12 May 2021 • Wenshuo Li, Hanting Chen, Mingqiang Huang, Xinghao Chen, Chunjing Xu, Yunhe Wang
Adder neural network (AdderNet) is a new kind of deep model that replaces the original massive multiplications in convolutions by additions while preserving the high performance.
no code implementations • 29 May 2021 • Hanting Chen, Yunhe Wang, Chang Xu, Chao Xu, Chunjing Xu, Tong Zhang
The widely-used convolutions in deep neural networks are exactly cross-correlation to measure the similarity between input feature and convolution filters, which involves massive multiplications between float values.
no code implementations • CVPR 2021 • Yiman Zhang, Hanting Chen, Xinghao Chen, Yiping Deng, Chunjing Xu, Yunhe Wang
Experiments on various datasets and architectures demonstrate that the proposed method is able to be utilized for effectively learning portable student networks without the original data, e. g., with 0. 16dB PSNR drop on Set5 for x2 super resolution.
no code implementations • 29 Sep 2021 • Lin Xinyang, Hanting Chen, Yixing Xu, Chao Xu, Xiaolin Gui, Yiping Deng, Yunhe Wang
We study the problem of learning from positive and unlabeled (PU) data in the federated setting, where each client only labels a little part of their dataset due to the limitation of resources and time.
1 code implementation • CVPR 2023 • Xudong Huang, Wei Li, Jie Hu, Hanting Chen, Yunhe Wang
We present Reference-guided Super-Resolution Neural Radiance Field (RefSR-NeRF) that extends NeRF to super resolution and photorealistic novel view synthesis.
no code implementations • 25 Sep 2023 • Yun Zhang, Wei Li, Simiao Li, Jie Hu, Hanting Chen, Hailing Wang, Zhijun Tu, Wenjia Wang, BingYi Jing, Yunhe Wang
In this paper, we put forth an approach from the perspective of effective data utilization, namely, the Data Upcycling Knowledge Distillation (DUKD), which facilitates the student model by the prior knowledge the teacher provided through the upcycled in-domain data derived from the input images.
Ranked #21 on Image Super-Resolution on Urban100 - 4x upscaling
no code implementations • 26 Sep 2023 • Hailing Wang, Wei Li, Yuanyuan Xi, Jie Hu, Hanting Chen, Longyu Li, Yunhe Wang
By matching similar patches between frames, objects with large motion ranges in dynamic scenes can be aligned, which can effectively alleviate the generation of artifacts.
no code implementations • 13 Dec 2023 • Xin Ding, Xiaoyu Liu, Zhijun Tu, Yun Zhang, Wei Li, Jie Hu, Hanting Chen, Yehui Tang, Zhiwei Xiong, Baoqun Yin, Yunhe Wang
Post-training quantization (PTQ) has played a key role in compressing large language models (LLMs) with ultra-low costs.
no code implementations • 27 Dec 2023 • Yunhe Wang, Hanting Chen, Yehui Tang, Tianyu Guo, Kai Han, Ying Nie, Xutao Wang, Hailin Hu, Zheyuan Bai, Yun Wang, Fangcheng Liu, Zhicheng Liu, Jianyuan Guo, Sinan Zeng, Yinchen Zhang, Qinghua Xu, Qun Liu, Jun Yao, Chao Xu, DaCheng Tao
We then demonstrate that the proposed approach is significantly effective for enhancing the model nonlinearity through carefully designed ablations; thus, we present a new efficient model architecture for establishing modern, namely, PanGu-$\pi$.
no code implementations • 25 Mar 2024 • Quan Zhang, Xiaoyu Liu, Wei Li, Hanting Chen, Junchao Liu, Jie Hu, Zhiwei Xiong, Chun Yuan, Yunhe Wang
SPD leverages a self-distillation manner to distill the fused semantic priors to boost the performance of original IR models.