Search Results for author: Yunhe Wang

Found 87 papers, 39 papers with code

Source-Free Domain Adaptation via Distribution Estimation

no code implementations24 Apr 2022 Ning Ding, Yixing Xu, Yehui Tang, Chao Xu, Yunhe Wang, DaCheng Tao

Domain Adaptation aims to transfer the knowledge learned from a labeled source domain to an unlabeled target domain whose data distributions are different.

Domain Adaptation

Multimodal Token Fusion for Vision Transformers

no code implementations19 Apr 2022 Yikai Wang, Xinghao Chen, Lele Cao, Wenbing Huang, Fuchun Sun, Yunhe Wang

Many adaptations of transformers have emerged to address the single-modal vision tasks, where self-attention modules are stacked to handle input sources like images.

3D Object Detection Image-to-Image Translation +1

MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation

no code implementations28 Mar 2022 Min Zhong, Xinghao Chen, Xiaokang Chen, Gang Zeng, Yunhe Wang

For instance, our approach achieves a 66. 4\% mAP with the 0. 5 IoU threshold on the ScanNetV2 test set, which is 1. 9\% higher than the state-of-the-art method.

3D Instance Segmentation Semantic Segmentation

Brain-inspired Multilayer Perceptron with Spiking Neurons

no code implementations28 Mar 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.

GhostNets on Heterogeneous Devices via Cheap Operations

2 code implementations10 Jan 2022 Kai Han, Yunhe Wang, Chang Xu, Jianyuan Guo, Chunjing Xu, Enhua Wu, Qi Tian

The proposed C-Ghost module can be taken as a plug-and-play component to upgrade existing convolutional neural networks.

PyramidTNT: Improved Transformer-in-Transformer Baselines with Pyramid Architecture

1 code implementation4 Jan 2022 Kai Han, Jianyuan Guo, Yehui Tang, Yunhe Wang

We hope this new baseline will be helpful to the further research and application of vision transformer.

An Empirical Study of Adder Neural Networks for Object Detection

no code implementations NeurIPS 2021 Xinghao Chen, Chang Xu, Minjing Dong, Chunjing Xu, Yunhe Wang

Adder neural networks (AdderNets) have shown impressive performance on image classification with only addition operations, which are more energy efficient than traditional convolutional neural networks built with multiplications.

Autonomous Driving Face Detection +2

Towards Stable and Robust AdderNets

no code implementations NeurIPS 2021 Minjing Dong, Yunhe Wang, Xinghao Chen, Chang Xu

Adder neural network (AdderNet) replaces the original convolutions with massive multiplications by cheap additions while achieving comparable performance thus yields a series of energy-efficient neural networks.

Adversarial Robustness

Adder Attention for Vision Transformer

no 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.

Handling Long-tailed Feature Distribution in AdderNets

no code implementations NeurIPS 2021 Minjing Dong, Yunhe Wang, Xinghao Chen, Chang Xu

Adder neural networks (ANNs) are designed for low energy cost which replace expensive multiplications in convolutional neural networks (CNNs) with cheaper additions to yield energy-efficient neural networks and hardware accelerations.

Knowledge Distillation

An Image Patch is a Wave: Phase-Aware Vision MLP

4 code implementations24 Nov 2021 Yehui Tang, Kai Han, Jianyuan Guo, Chang Xu, Yanxi Li, Chao Xu, Yunhe Wang

To dynamically aggregate tokens, we propose to represent each token as a wave function with two parts, amplitude and phase.

Image Classification Object Detection +1

Positive and Unlabeled Federated Learning

no code implementations29 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.

Federated Learning

Learning Versatile Convolution Filters for Efficient Visual Recognition

no code implementations20 Sep 2021 Kai Han, Yunhe Wang, Chang Xu, Chunjing Xu, Enhua Wu, DaCheng Tao

A series of secondary filters can be derived from a primary filter with the help of binary masks.

Hire-MLP: Vision MLP via Hierarchical Rearrangement

3 code implementations30 Aug 2021 Jianyuan Guo, Yehui Tang, Kai Han, Xinghao Chen, Han Wu, Chao Xu, Chang Xu, Yunhe Wang

Previous vision MLPs such as MLP-Mixer and ResMLP accept linearly flattened image patches as input, making them inflexible for different input sizes and hard to capture spatial information.

Image Classification Object Detection +1

Neural Architecture Dilation for Adversarial Robustness

no code implementations NeurIPS 2021 Yanxi Li, Zhaohui Yang, Yunhe Wang, Chang Xu

With the tremendous advances in the architecture and scale of convolutional neural networks (CNNs) over the past few decades, they can easily reach or even exceed the performance of humans in certain tasks.

Adversarial Robustness

Greedy Network Enlarging

no code implementations31 Jul 2021 Chuanjian Liu, Kai Han, An Xiao, Yiping Deng, Wei zhang, Chunjing Xu, Yunhe Wang

Recent studies on deep convolutional neural networks present a simple paradigm of architecture design, i. e., models with more MACs typically achieve better accuracy, such as EfficientNet and RegNet.

CMT: Convolutional Neural Networks Meet Vision Transformers

7 code implementations13 Jul 2021 Jianyuan Guo, Kai Han, Han Wu, Chang Xu, Yehui Tang, Chunjing Xu, Yunhe Wang

Vision transformers have been successfully applied to image recognition tasks due to their ability to capture long-range dependencies within an image.

Augmented Shortcuts for Vision Transformers

3 code implementations NeurIPS 2021 Yehui Tang, Kai Han, Chang Xu, An Xiao, Yiping Deng, Chao Xu, Yunhe Wang

Transformer models have achieved great progress on computer vision tasks recently.

Post-Training Quantization for Vision Transformer

no code implementations NeurIPS 2021 Zhenhua Liu, Yunhe Wang, Kai Han, Siwei Ma, Wen Gao

Recently, transformer has achieved remarkable performance on a variety of computer vision applications.

Quantization

Federated Learning with Positive and Unlabeled Data

no code implementations21 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.

Federated Learning

ReNAS: Relativistic Evaluation of Neural Architecture Search

2 code implementations CVPR 2021 Yixing Xu, Yunhe Wang, Kai Han, Yehui Tang, Shangling Jui, Chunjing Xu, Chang Xu

An effective and efficient architecture performance evaluation scheme is essential for the success of Neural Architecture Search (NAS).

Neural Architecture Search

Positive-Unlabeled Data Purification in the Wild for Object Detection

no code implementations CVPR 2021 Jianyuan Guo, Kai Han, Han Wu, Chao Zhang, Xinghao Chen, Chunjing Xu, Chang Xu, Yunhe Wang

In this paper, we present a positive-unlabeled learning based scheme to expand training data by purifying valuable images from massive unlabeled ones, where the original training data are viewed as positive data and the unlabeled images in the wild are unlabeled data.

Knowledge Distillation Object Detection

Learning Student Networks in the Wild

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.

Knowledge Distillation Model Compression

Data-Free Knowledge Distillation for Image Super-Resolution

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.

Image Super-Resolution Knowledge Distillation +1

Dynamic Resolution Network

2 code implementations NeurIPS 2021 Mingjian Zhu, Kai Han, Enhua Wu, Qiulin Zhang, Ying Nie, Zhenzhong Lan, Yunhe Wang

To this end, we propose a novel dynamic-resolution network (DRNet) in which the input resolution is determined dynamically based on each input sample.

Patch Slimming for Efficient Vision Transformers

no code implementations5 Jun 2021 Yehui Tang, Kai Han, Yunhe Wang, Chang Xu, Jianyuan Guo, Chao Xu, DaCheng Tao

We first identify the effective patches in the last layer and then use them to guide the patch selection process of previous layers.

Universal Adder Neural Networks

no code implementations29 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.

Winograd Algorithm for AdderNet

no code implementations12 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.

Coarse-to-Fine Searching for Efficient Generative Adversarial Networks

no code implementations19 Apr 2021 Jiahao Wang, Han Shu, Weihao Xia, Yujiu Yang, Yunhe Wang

This paper studies the neural architecture search (NAS) problem for developing efficient generator networks.

Image Generation Neural Architecture Search

Distilling Object Detectors via Decoupled Features

1 code implementation CVPR 2021 Jianyuan Guo, Kai Han, Yunhe Wang, Han Wu, Xinghao Chen, Chunjing Xu, Chang Xu

To this end, we present a novel distillation algorithm via decoupled features (DeFeat) for learning a better student detector.

Knowledge Distillation Object Detection

Learning Frequency-aware Dynamic Network for Efficient Super-Resolution

no code implementations ICCV 2021 Wenbin Xie, Dehua Song, Chang Xu, Chunjing Xu, HUI ZHANG, Yunhe Wang

Extensive experiments conducted on benchmark SISR models and datasets show that the frequency-aware dynamic network can be employed for various SISR neural architectures to obtain the better tradeoff between visual quality and computational complexity.

Image Super-Resolution

Manifold Regularized Dynamic Network Pruning

2 code implementations CVPR 2021 Yehui Tang, Yunhe Wang, Yixing Xu, Yiping Deng, Chao Xu, DaCheng Tao, Chang Xu

Then, the manifold relationship between instances and the pruned sub-networks will be aligned in the training procedure.

Network Pruning

Learning Frequency Domain Approximation for Binary Neural Networks

1 code implementation NeurIPS 2021 Yixing Xu, Kai Han, Chang Xu, Yehui Tang, Chunjing Xu, Yunhe Wang

Binary neural networks (BNNs) represent original full-precision weights and activations into 1-bit with sign function.

Transformer in Transformer

10 code implementations NeurIPS 2021 Kai Han, An Xiao, Enhua Wu, Jianyuan Guo, Chunjing Xu, Yunhe Wang

In this paper, we point out that the attention inside these local patches are also essential for building visual transformers with high performance and we explore a new architecture, namely, Transformer iN Transformer (TNT).

Fine-Grained Image Classification

AdderNet and its Minimalist Hardware Design for Energy-Efficient Artificial Intelligence

no code implementations25 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.

Quantization

GhostSR: Learning Ghost Features for Efficient Image Super-Resolution

no code implementations21 Jan 2021 Ying Nie, Kai Han, Zhenhua Liu, An Xiao, Yiping Deng, Chunjing Xu, Yunhe Wang

Based on the observation that many features in SISR models are also similar to each other, we propose to use shift operation to generate the redundant features (i. e., Ghost features).

Image Super-Resolution

A Survey on Vision Transformer

no code implementations23 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.

Image Classification

Model Rubik’s Cube: Twisting Resolution, Depth and Width for TinyNets

2 code implementations NeurIPS 2020 Kai Han, Yunhe Wang, Qiulin Zhang, Wei zhang, Chunjing Xu, Tong Zhang

To this end, we summarize a tiny formula for downsizing neural architectures through a series of smaller models derived from the EfficientNet-B0 with the FLOPs constraint.

Image Classification

Dynamic Feature Pyramid Networks for Object Detection

1 code implementation1 Dec 2020 Mingjian Zhu, Kai Han, Changbin Yu, Yunhe Wang

An attempt to enhance the FPN is enriching the spatial information by expanding the receptive fields, which is promising to largely improve the detection accuracy.

Object Detection

Pre-Trained Image Processing Transformer

3 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)

Color Image Denoising Contrastive Learning +2

Residual Distillation: Towards Portable Deep Neural Networks without Shortcuts

no code implementations NeurIPS 2020 Guilin Li, Junlei Zhang, Yunhe Wang, Chuanjian Liu, Matthias Tan, Yunfeng Lin, Wei zhang, Jiashi Feng, Tong Zhang

In particular, we propose a novel joint-training framework to train plain CNN by leveraging the gradients of the ResNet counterpart.

Adapting Neural Architectures Between Domains

1 code implementation NeurIPS 2020 Yanxi Li, Zhaohui Yang, Yunhe Wang, Chang Xu

The power of deep neural networks is to be unleashed for analyzing a large volume of data (e. g. ImageNet), but the architecture search is often executed on another smaller dataset (e. g. CIFAR-10) to finish it in a feasible time.

Domain Adaptation Generalization Bounds +1

Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets

5 code implementations28 Oct 2020 Kai Han, Yunhe Wang, Qiulin Zhang, Wei zhang, Chunjing Xu, Tong Zhang

To this end, we summarize a tiny formula for downsizing neural architectures through a series of smaller models derived from the EfficientNet-B0 with the FLOPs constraint.

Image Classification

SCOP: Scientific Control for Reliable Neural Network Pruning

4 code implementations NeurIPS 2020 Yehui Tang, Yunhe Wang, Yixing Xu, DaCheng Tao, Chunjing Xu, Chao Xu, Chang Xu

To increase the reliability of the results, we prefer to have a more rigorous research design by including a scientific control group as an essential part to minimize the effect of all factors except the association between the filter and expected network output.

Network Pruning

Kernel Based Progressive Distillation for Adder Neural Networks

no code implementations NeurIPS 2020 Yixing Xu, Chang Xu, Xinghao Chen, Wei zhang, Chunjing Xu, Yunhe Wang

A convolutional neural network (CNN) with the same architecture is simultaneously initialized and trained as a teacher network, features and weights of ANN and CNN will be transformed to a new space to eliminate the accuracy drop.

Knowledge Distillation

AdderSR: Towards Energy Efficient Image Super-Resolution

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.

Image Classification Image Super-Resolution

Adversarially Robust Neural Architectures

no code implementations2 Sep 2020 Minjing Dong, Yanxi Li, Yunhe Wang, Chang Xu

This paper thus aims to improve the adversarial robustness of the network from the architecture perspective with NAS framework.

Adversarial Attack Adversarial Robustness

MTP: Multi-Task Pruning for Efficient Semantic Segmentation Networks

no code implementations16 Jul 2020 Xinghao Chen, Yiman Zhang, Yunhe Wang

To identify the redundancy in segmentation networks, we present a multi-task channel pruning approach.

Classification General Classification +2

DC-NAS: Divide-and-Conquer Neural Architecture Search

no code implementations29 May 2020 Yunhe Wang, Yixing Xu, DaCheng Tao

Neural architecture searching is a way of automatically exploring optimal deep neural networks in a given huge search space.

Neural Architecture Search

HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens

1 code implementation CVPR 2021 Zhaohui Yang, Yunhe Wang, Xinghao Chen, Jianyuan Guo, Wei zhang, Chao Xu, Chunjing Xu, DaCheng Tao, Chang Xu

To achieve an extremely fast NAS while preserving the high accuracy, we propose to identify the vital blocks and make them the priority in the architecture search.

Neural Architecture Search

A Semi-Supervised Assessor of Neural Architectures

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.

Neural Architecture Search

Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection

1 code implementation CVPR 2020 Jianyuan Guo, Kai Han, Yunhe Wang, Chao Zhang, Zhaohui Yang, Han Wu, Xinghao Chen, Chang Xu

To this end, we propose a hierarchical trinity search framework to simultaneously discover efficient architectures for all components (i. e. backbone, neck, and head) of object detector in an end-to-end manner.

Image Classification Neural Architecture Search +1

Distilling portable Generative Adversarial Networks for Image Translation

no code implementations7 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.

Image-to-Image Translation Knowledge Distillation +1

Automatically Searching for U-Net Image Translator Architecture

no code implementations26 Feb 2020 Han Shu, Yunhe Wang

Moreover, we transplant the searched network architecture to other datasets which are not involved in the architecture searching procedure.

Semantic Segmentation

Beyond Dropout: Feature Map Distortion to Regularize Deep Neural Networks

2 code implementations23 Feb 2020 Yehui Tang, Yunhe Wang, Yixing Xu, Boxin Shi, Chao Xu, Chunjing Xu, Chang Xu

On one hand, massive trainable parameters significantly enhance the performance of these deep networks.

Discernible Image Compression

no code implementations17 Feb 2020 Zhaohui Yang, Yunhe Wang, Chang Xu, Peng Du, Chao Xu, Chunjing Xu, Qi Tian

Experiments on benchmarks demonstrate that images compressed by using the proposed method can also be well recognized by subsequent visual recognition and detection models.

Image Compression Object Detection

On Positive-Unlabeled Classification in GAN

1 code implementation CVPR 2020 Tianyu Guo, Chang Xu, Jiajun Huang, Yunhe Wang, Boxin Shi, Chao Xu, DaCheng Tao

In contrast, it is more reasonable to treat the generated data as unlabeled, which could be positive or negative according to their quality.

Classification General Classification

Widening and Squeezing: Towards Accurate and Efficient QNNs

no code implementations3 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.

Quantization

AdderNet: Do We Really Need Multiplications in Deep Learning?

2 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.

GhostNet: More Features from Cheap Operations

13 code implementations CVPR 2020 Kai Han, Yunhe Wang, Qi Tian, Jianyuan Guo, Chunjing Xu, Chang Xu

Deploying convolutional neural networks (CNNs) on embedded devices is difficult due to the limited memory and computation resources.

Image Classification

ReNAS:Relativistic Evaluation of Neural Architecture Search

3 code implementations30 Sep 2019 Yixing Xu, Yunhe Wang, Kai Han, Yehui Tang, Shangling Jui, Chunjing Xu, Chang Xu

An effective and efficient architecture performance evaluation scheme is essential for the success of Neural Architecture Search (NAS).

Neural Architecture Search

Efficient Residual Dense Block Search for Image Super-Resolution

1 code implementation25 Sep 2019 Dehua Song, Chang Xu, Xu Jia, Yiyi Chen, Chunjing Xu, Yunhe Wang

Focusing on this issue, we propose an efficient residual dense block search algorithm with multiple objectives to hunt for fast, lightweight and accurate networks for image super-resolution.

Image Super-Resolution

Positive-Unlabeled Compression on the Cloud

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.

Knowledge Distillation

Searching for Accurate Binary Neural Architectures

no code implementations16 Sep 2019 Mingzhu Shen, Kai Han, Chunjing Xu, Yunhe Wang

Binary neural networks have attracted tremendous attention due to the efficiency for deploying them on mobile devices.

Frame

CARS: Continuous Evolution for Efficient Neural Architecture Search

1 code implementation CVPR 2020 Zhaohui Yang, Yunhe Wang, Xinghao Chen, Boxin Shi, Chao Xu, Chunjing Xu, Qi Tian, Chang Xu

Architectures in the population that share parameters within one SuperNet in the latest generation will be tuned over the training dataset with a few epochs.

Neural Architecture Search

Full-Stack Filters to Build Minimum Viable CNNs

1 code implementation6 Aug 2019 Kai Han, Yunhe Wang, Yixing Xu, Chunjing Xu, DaCheng Tao, Chang Xu

Existing works used to decrease the number or size of requested convolution filters for a minimum viable CNN on edge devices.

Learning Instance-wise Sparsity for Accelerating Deep Models

no code implementations27 Jul 2019 Chuanjian Liu, Yunhe Wang, Kai Han, Chunjing Xu, Chang Xu

Exploring deep convolutional neural networks of high efficiency and low memory usage is very essential for a wide variety of machine learning tasks.

Attribute Aware Pooling for Pedestrian Attribute Recognition

no code implementations27 Jul 2019 Kai Han, Yunhe Wang, Han Shu, Chuanjian Liu, Chunjing Xu, Chang Xu

This paper expands the strength of deep convolutional neural networks (CNNs) to the pedestrian attribute recognition problem by devising a novel attribute aware pooling algorithm.

Pedestrian Attribute Recognition

Co-Evolutionary Compression for Unpaired Image Translation

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.

Image-to-Image Translation Translation

Data-Free Learning of Student Networks

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.

Neural Network Compression

Learning Student Networks via Feature Embedding

no code implementations17 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.

Knowledge Distillation

AutoEncoder Inspired Unsupervised Feature Selection

1 code implementation23 Oct 2017 Kai Han, Yunhe Wang, Chao Zhang, Chao Li, Chao Xu

High-dimensional data in many areas such as computer vision and machine learning tasks brings in computational and analytical difficulty.

Beyond Filters: Compact Feature Map for Portable Deep Model

1 code implementation ICML 2017 Yunhe Wang, Chang Xu, Chao Xu, DaCheng Tao

The filter is then re-configured to establish the mapping from original input to the new compact feature map, and the resulting network can preserve intrinsic information of the original network with significantly fewer parameters, which not only decreases the online memory for launching CNN but also accelerates the computation speed.

Towards Evolutional Compression

no code implementations25 Jul 2017 Yunhe Wang, Chang Xu, Jiayan Qiu, Chao Xu, DaCheng Tao

In contrast to directly recognizing subtle weights or filters as redundant in a given CNN, this paper presents an evolutionary method to automatically eliminate redundant convolution filters.

Privileged Multi-label Learning

no code implementations25 Jan 2017 Shan You, Chang Xu, Yunhe Wang, Chao Xu, DaCheng Tao

This paper presents privileged multi-label learning (PrML) to explore and exploit the relationship between labels in multi-label learning problems.

Multi-Label Learning

Parts for the Whole: The DCT Norm for Extreme Visual Recovery

no code implementations19 Apr 2016 Yunhe Wang, Chang Xu, Shan You, DaCheng Tao, Chao Xu

Here we study the extreme visual recovery problem, in which over 90\% of pixel values in a given image are missing.

Streaming Label Learning for Modeling Labels on the Fly

no code implementations19 Apr 2016 Shan You, Chang Xu, Yunhe Wang, Chao Xu, DaCheng Tao

The core of SLL is to explore and exploit the relationships between new labels and past labels and then inherit the relationship into hypotheses of labels to boost the performance of new classifiers.

Multi-Label Learning

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