Search Results for author: Pan Zhou

Found 84 papers, 20 papers with code

Towards Understanding Why Lookahead Generalizes Better Than SGD and Beyond

no code implementations NeurIPS 2021 Pan Zhou, Hanshu Yan, Xiaotong Yuan, Jiashi Feng, Shuicheng Yan

Specifically, we prove that lookahead using SGD as its inner-loop optimizer can better balance the optimization error and generalization error to achieve smaller excess risk error than vanilla SGD on (strongly) convex problems and nonconvex problems with Polyak-{\L}ojasiewicz condition which has been observed/proved in neural networks.

MetaFormer is Actually What You Need for Vision

2 code implementations22 Nov 2021 Weihao Yu, Mi Luo, Pan Zhou, Chenyang Si, Yichen Zhou, Xinchao Wang, Jiashi Feng, Shuicheng Yan

Based on this observation, we hypothesize that the general architecture of the transformers, instead of the specific token mixer module, is more essential to the model's performance.

Image Classification Semantic Segmentation

Bandits for Black-box Attacks to Graph Neural Networks with Structure Perturbation

no code implementations29 Sep 2021 Binghui Wang, Youqi Li, Pan Zhou

However, many recent works have demonstrated that an attacker can mislead GNN models by slightly perturbing the graph structure.

Graph Classification Node Classification

Backdoor Attacks on Federated Learning with Lottery Ticket Hypothesis

1 code implementation22 Sep 2021 Zeyuan Yin, Ye Yuan, Panfeng Guo, Pan Zhou

Edge devices in federated learning usually have much more limited computation and communication resources compared to servers in a data center.

Federated Learning Model Compression

Progressively Guide to Attend: An Iterative Alignment Framework for Temporal Sentence Grounding

no code implementations EMNLP 2021 Daizong Liu, Xiaoye Qu, Pan Zhou

A key solution to temporal sentence grounding (TSG) exists in how to learn effective alignment between vision and language features extracted from an untrimmed video and a sentence description.

Adaptive Proposal Generation Network for Temporal Sentence Localization in Videos

no code implementations EMNLP 2021 Daizong Liu, Xiaoye Qu, Jianfeng Dong, Pan Zhou

However, the performance of bottom-up model is inferior to the top-down counterpart as it fails to exploit the segment-level interaction.

Coarse to Fine: Domain Adaptive Crowd Counting via Adversarial Scoring Network

no code implementations27 Jul 2021 Zhikang Zou, Xiaoye Qu, Pan Zhou, Shuangjie Xu, Xiaoqing Ye, Wenhao Wu, Jin Ye

In specific, at the coarse-grained stage, we design a dual-discriminator strategy to adapt source domain to be close to the targets from the perspectives of both global and local feature space via adversarial learning.

Crowd Counting Transfer Learning

A Theory-Driven Self-Labeling Refinement Method for Contrastive Representation Learning

no code implementations NeurIPS 2021 Pan Zhou, Caiming Xiong, Xiao-Tong Yuan, Steven Hoi

Although intuitive, such a native label assignment strategy cannot reveal the underlying semantic similarity between a query and its positives and negatives, and impairs performance, since some negatives are semantically similar to the query or even share the same semantic class as the query.

Contrastive Learning Representation Learning +2

Prototypical Graph Contrastive Learning

no code implementations17 Jun 2021 Shuai Lin, Pan Zhou, Zi-Yuan Hu, Shuojia Wang, Ruihui Zhao, Yefeng Zheng, Liang Lin, Eric Xing, Xiaodan Liang

However, since for a query, its negatives are uniformly sampled from all graphs, existing methods suffer from the critical sampling bias issue, i. e., the negatives likely having the same semantic structure with the query, leading to performance degradation.

Contrastive Learning Unsupervised Representation Learning

Emotion-aware Chat Machine: Automatic Emotional Response Generation for Human-like Emotional Interaction

no code implementations6 Jun 2021 Wei Wei, Jiayi Liu, Xianling Mao, Guibing Guo, Feida Zhu, Pan Zhou, Yuchong Hu

The consistency of a response to a given post at semantic-level and emotional-level is essential for a dialogue system to deliver human-like interactions.

Exploiting Global Contextual Information for Document-level Named Entity Recognition

no code implementations2 Jun 2021 Zanbo Wang, Wei Wei, Xianling Mao, Shanshan Feng, Pan Zhou, Zhiyong He, Sheng Jiang

To this end, we propose a model called Global Context enhanced Document-level NER (GCDoc) to leverage global contextual information from two levels, i. e., both word and sentence.

Document-level Named Entity Recognition +1

TRS: Transferability Reduced Ensemble via Promoting Gradient Diversity and Model Smoothness

no code implementations NeurIPS 2021 Zhuolin Yang, Linyi Li, Xiaojun Xu, Shiliang Zuo, Qian Chen, Pan Zhou, Benjamin I. P. Rubinstein, Ce Zhang, Bo Li

To answer these questions, in this work we first theoretically analyze and outline sufficient conditions for adversarial transferability between models; then propose a practical algorithm to reduce the transferability between base models within an ensemble to improve its robustness.

RBNN: Memory-Efficient Reconfigurable Deep Binary Neural Network with IP Protection for Internet of Things

no code implementations9 May 2021 Huming Qiu, Hua Ma, Zhi Zhang, Yifeng Zheng, Anmin Fu, Pan Zhou, Yansong Gao, Derek Abbott, Said F. Al-Sarawi

To this end, a 1-bit quantized DNN model or deep binary neural network maximizes the memory efficiency, where each parameter in a BNN model has only 1-bit.


WNARS: WFST based Non-autoregressive Streaming End-to-End Speech Recognition

no code implementations8 Apr 2021 Zhichao Wang, Wenwen Yang, Pan Zhou, Wei Chen

Recently, attention-based encoder-decoder (AED) end-to-end (E2E) models have drawn more and more attention in the field of automatic speech recognition (ASR).

automatic-speech-recognition End-To-End Speech Recognition +1

TRS: Transferability Reduced Ensemble via Encouraging Gradient Diversity and Model Smoothness

1 code implementation NeurIPS 2021 Zhuolin Yang, Linyi Li, Xiaojun Xu, Shiliang Zuo, Qian Chen, Benjamin Rubinstein, Pan Zhou, Ce Zhang, Bo Li

To answer these questions, in this work we first theoretically analyze and outline sufficient conditions for adversarial transferability between models; then propose a practical algorithm to reduce the transferability between base models within an ensemble to improve its robustness.

Context-aware Biaffine Localizing Network for Temporal Sentence Grounding

1 code implementation CVPR 2021 Daizong Liu, Xiaoye Qu, Jianfeng Dong, Pan Zhou, Yu Cheng, Wei Wei, Zichuan Xu, Yulai Xie

This paper addresses the problem of temporal sentence grounding (TSG), which aims to identify the temporal boundary of a specific segment from an untrimmed video by a sentence query.

Erasure for Advancing: Dynamic Self-Supervised Learning for Commonsense Reasoning

no code implementations1 Jan 2021 Fuyu Wang, Pan Zhou, Xiaodan Liang, Liang Lin

To solve this issue, we propose a novel DynamIc Self-sUperviSed Erasure (DISUSE) which adaptively erases redundant and artifactual clues in the context and questions to learn and establish the correct corresponding pair relations between the questions and their clues.

Question Answering Self-Supervised Learning +1

Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue Generation

1 code implementation22 Dec 2020 Shuai Lin, Pan Zhou, Xiaodan Liang, Jianheng Tang, Ruihui Zhao, Ziliang Chen, Liang Lin

Besides, we develop a Graph-Evolving Meta-Learning (GEML) framework that learns to evolve the commonsense graph for reasoning disease-symptom correlations in a new disease, which effectively alleviates the needs of a large number of dialogues.

Dialogue Generation Meta-Learning

Spatiotemporal Graph Neural Network based Mask Reconstruction for Video Object Segmentation

no code implementations10 Dec 2020 Daizong Liu, Shuangjie Xu, Xiao-Yang Liu, Zichuan Xu, Wei Wei, Pan Zhou

To capture temporal information from previous frames, we use a memory network to refine the mask of current frame by retrieving historic masks in a temporal graph.

Fine-tuning One-shot visual object segmentation +3

F2Net: Learning to Focus on the Foreground for Unsupervised Video Object Segmentation

no code implementations4 Dec 2020 Daizong Liu, Dongdong Yu, Changhu Wang, Pan Zhou

Specifically, our proposed network consists of three main parts: Siamese Encoder Module, Center Guiding Appearance Diffusion Module, and Dynamic Information Fusion Module.

Semantic Segmentation Unsupervised Video Object Segmentation +1

Reasoning Step-by-Step: Temporal Sentence Localization in Videos via Deep Rectification-Modulation Network

no code implementations COLING 2020 Daizong Liu, Xiaoye Qu, Jianfeng Dong, Pan Zhou

In this paper, we propose a novel deep rectification-modulation network (RMN), transforming this task into a multi-step reasoning process by repeating rectification and modulation.


V3H: View Variation and View Heredity for Incomplete Multi-view Clustering

1 code implementation23 Nov 2020 Xiang Fang, Yuchong Hu, Pan Zhou, Dapeng Oliver Wu

Inspired by the variation and the heredity in genetics, V3H first decomposes each subspace into a variation matrix for the corresponding view and a heredity matrix for all the views to represent the unique information and the consistent information respectively.

Incomplete multi-view clustering

ANIMC: A Soft Framework for Auto-weighted Noisy and Incomplete Multi-view Clustering

1 code implementation20 Nov 2020 Xiang Fang, Yuchong Hu, Pan Zhou, Dapeng Oliver Wu

In these scenarios, original image data often contain missing instances and noises, which is ignored by most multi-view clustering methods.

Incomplete multi-view clustering

Unbalanced Incomplete Multi-view Clustering via the Scheme of View Evolution: Weak Views are Meat; Strong Views do Eat

1 code implementation20 Nov 2020 Xiang Fang, Yuchong Hu, Pan Zhou, Dapeng Oliver Wu

However, different views often have distinct incompleteness, i. e., unbalanced incompleteness, which results in strong views (low-incompleteness views) and weak views (high-incompleteness views).

Incomplete multi-view clustering Multi-view Subspace Clustering

User-based Network Embedding for Collective Opinion Spammer Detection

no code implementations16 Nov 2020 Ziyang Wang, Wei Wei, Xian-Ling Mao, Guibing Guo, Pan Zhou, Shanshan Feng

Due to the huge commercial interests behind online reviews, a tremendousamount of spammers manufacture spam reviews for product reputation manipulation.

Network Embedding

Target Guided Emotion Aware Chat Machine

no code implementations15 Nov 2020 Wei Wei, Jiayi Liu, Xianling Mao, Guibin Guo, Feida Zhu, Pan Zhou, Yuchong Hu, Shanshan Feng

The consistency of a response to a given post at semantic-level and emotional-level is essential for a dialogue system to deliver human-like interactions.

Video-based Facial Expression Recognition using Graph Convolutional Networks

no code implementations26 Oct 2020 Daizong Liu, Hongting Zhang, Pan Zhou

In terms of video based FER task, it is sensible to capture the dynamic expression variation among the frames to recognize facial expression.

Facial Expression Recognition

Iterative Graph Self-Distillation

no code implementations23 Oct 2020 HANLIN ZHANG, Shuai Lin, Weiyang Liu, Pan Zhou, Jian Tang, Xiaodan Liang, Eric P. Xing

How to discriminatively vectorize graphs is a fundamental challenge that attracts increasing attentions in recent years.

Contrastive Learning Graph Learning +1

Towards Theoretically Understanding Why SGD Generalizes Better Than ADAM in Deep Learning

no code implementations NeurIPS 2020 Pan Zhou, Jiashi Feng, Chao Ma, Caiming Xiong, Steven Hoi, Weinan E

The result shows that (1) the escaping time of both SGD and ADAM~depends on the Radon measure of the basin positively and the heaviness of gradient noise negatively; (2) for the same basin, SGD enjoys smaller escaping time than ADAM, mainly because (a) the geometry adaptation in ADAM~via adaptively scaling each gradient coordinate well diminishes the anisotropic structure in gradient noise and results in larger Radon measure of a basin; (b) the exponential gradient average in ADAM~smooths its gradient and leads to lighter gradient noise tails than SGD.

How Important is the Train-Validation Split in Meta-Learning?

no code implementations12 Oct 2020 Yu Bai, Minshuo Chen, Pan Zhou, Tuo Zhao, Jason D. Lee, Sham Kakade, Huan Wang, Caiming Xiong

A common practice in meta-learning is to perform a train-validation split (\emph{train-val method}) where the prior adapts to the task on one split of the data, and the resulting predictor is evaluated on another split.


Hybrid Stochastic-Deterministic Minibatch Proximal Gradient: Less-Than-Single-Pass Optimization with Nearly Optimal Generalization

no code implementations ICML 2020 Pan Zhou, Xiao-Tong Yuan

Particularly, in the case of $\epsilon=\mathcal{O}\big(1/\sqrt{n}\big)$ which is at the order of intrinsic excess error bound of a learning model and thus sufficient for generalization, the stochastic gradient complexity bounds of HSDMPG for quadratic and generic loss functions are respectively $\mathcal{O} (n^{0. 875}\log^{1. 5}(n))$ and $\mathcal{O} (n^{0. 875}\log^{2. 25}(n))$, which to our best knowledge, for the first time achieve optimal generalization in less than a single pass over data.

Reinforcement Learning-based Black-Box Evasion Attacks to Link Prediction in Dynamic Graphs

no code implementations1 Sep 2020 Houxiang Fan, Binghui Wang, Pan Zhou, Ang Li, Meng Pang, Zichuan Xu, Cai Fu, Hai Li, Yiran Chen

Link prediction in dynamic graphs (LPDG) is an important research problem that has diverse applications such as online recommendations, studies on disease contagion, organizational studies, etc.

Graph Embedding Link Prediction

Evasion Attacks to Graph Neural Networks via Influence Function

no code implementations1 Sep 2020 Binghui Wang, Tianxiang Zhou, Minhua Lin, Pan Zhou, Ang Li, Meng Pang, Cai Fu, Hai Li, Yiran Chen

Next, we reformulate the evasion attack against GNNs to be related to calculating label influence on LP, which is applicable to multi-layer GNNs and does not need to know the GNN model.

Node Classification

Fine-grained Iterative Attention Network for TemporalLanguage Localization in Videos

no code implementations6 Aug 2020 Xiaoye Qu, Pengwei Tang, Zhikang Zhou, Yu Cheng, Jianfeng Dong, Pan Zhou

In this paper, we propose a Fine-grained Iterative Attention Network (FIAN) that consists of an iterative attention module for bilateral query-video in-formation extraction.

Jointly Cross- and Self-Modal Graph Attention Network for Query-Based Moment Localization

1 code implementation4 Aug 2020 Daizong Liu, Xiaoye Qu, Xiao-Yang Liu, Jianfeng Dong, Pan Zhou, Zichuan Xu

To this end, we propose a novel Cross- and Self-Modal Graph Attention Network (CSMGAN) that recasts this task as a process of iterative messages passing over a joint graph.

Graph Attention

Theory-Inspired Path-Regularized Differential Network Architecture Search

1 code implementation NeurIPS 2020 Pan Zhou, Caiming Xiong, Richard Socher, Steven C. H. Hoi

Then we propose a theory-inspired path-regularized DARTS that consists of two key modules: (i) a differential group-structured sparse binary gate introduced for each operation to avoid unfair competition among operations, and (ii) a path-depth-wise regularization used to incite search exploration for deep architectures that often converge slower than shallow ones as shown in our theory and are not well explored during the search.

Image Classification

Federated Mutual Learning

1 code implementation27 Jun 2020 Tao Shen, Jie Zhang, Xinkang Jia, Fengda Zhang, Gang Huang, Pan Zhou, Kun Kuang, Fei Wu, Chao Wu

The experiments show that FML can achieve better performance than alternatives in typical FL setting, and clients can be benefited from FML with different models and tasks.

Federated Learning

Improving GAN Training with Probability Ratio Clipping and Sample Reweighting

1 code implementation NeurIPS 2020 Yue Wu, Pan Zhou, Andrew Gordon Wilson, Eric P. Xing, Zhiting Hu

Despite success on a wide range of problems related to vision, generative adversarial networks (GANs) often suffer from inferior performance due to unstable training, especially for text generation.

Image Generation Style Transfer +1

Prototypical Contrastive Learning of Unsupervised Representations

3 code implementations ICLR 2021 Junnan Li, Pan Zhou, Caiming Xiong, Steven C. H. Hoi

This paper presents Prototypical Contrastive Learning (PCL), an unsupervised representation learning method that addresses the fundamental limitations of instance-wise contrastive learning.

Contrastive Learning Self-Supervised Image Classification +3

AN-GCN: An Anonymous Graph Convolutional Network Defense Against Edge-Perturbing Attack

no code implementations6 May 2020 Ao Liu, Beibei Li, Tao Li, Pan Zhou, Rui Wang

In this paper, we first generalize the formulation of edge-perturbing attacks and strictly prove the vulnerability of GCNs to such attacks in node classification tasks.

Adversarial Attack Classification +3

Crowd Counting via Hierarchical Scale Recalibration Network

no code implementations7 Mar 2020 Zhikang Zou, Yifan Liu, Shuangjie Xu, Wei Wei, Shiping Wen, Pan Zhou

Extensive experiments on crowd counting datasets (ShanghaiTech, MALL, WorldEXPO'10, and UCSD) show that our HSRNet can deliver superior results over all state-of-the-art approaches.

Crowd Counting

Dynamic Graph Correlation Learning for Disease Diagnosis with Incomplete Labels

no code implementations26 Feb 2020 Daizong Liu, Shuangjie Xu, Pan Zhou, Kun He, Wei Wei, Zichuan Xu

In this work, we propose a Disease Diagnosis Graph Convolutional Network (DD-GCN) that presents a novel view of investigating the inter-dependency among different diseases by using a dynamic learnable adjacency matrix in graph structure to improve the diagnosis accuracy.

Curriculum Learning Multi-Label Classification

Transfer Heterogeneous Knowledge Among Peer-to-Peer Teammates: A Model Distillation Approach

no code implementations6 Feb 2020 Zeyue Xue, Shuang Luo, Chao Wu, Pan Zhou, Kaigui Bian, Wei Du

Peer-to-peer knowledge transfer in distributed environments has emerged as a promising method since it could accelerate learning and improve team-wide performance without relying on pre-trained teachers in deep reinforcement learning.

Model distillation Transfer Learning

Prophet: Proactive Candidate-Selection for Federated Learning by Predicting the Qualities of Training and Reporting Phases

no code implementations3 Feb 2020 Huawei Huang, Kangying Lin, Song Guo, Pan Zhou, Zibin Zheng

In the dynamic environment, the mobile devices selected by the existing reactive candidate-selection algorithms very possibly fail to complete the training and reporting phases of FL, because the FL parameter server only knows the currently-observed resources of all candidates.

Federated Learning

Efficient Meta Learning via Minibatch Proximal Update

no code implementations NeurIPS 2019 Pan Zhou, Xiao-Tong Yuan, Huan Xu, Shuicheng Yan, Jiashi Feng

We address the problem of meta-learning which learns a prior over hypothesis from a sample of meta-training tasks for fast adaptation on meta-testing tasks.

Few-Shot Learning Few-shot Regression

Tell-the-difference: Fine-grained Visual Descriptor via a Discriminating Referee

no code implementations14 Oct 2019 Shuangjie Xu, Feng Xu, Yu Cheng, Pan Zhou

In this paper, we investigate a novel problem of telling the difference between image pairs in natural language.

Image Captioning

Generating Robust Audio Adversarial Examples using Iterative Proportional Clipping

no code implementations25 Sep 2019 Hongting Zhang, Qiben Yan, Pan Zhou

We then impose a constraint on the perturbation at the positions with lower sound intensity across the time domain to eliminate the perceptible noise during the silent periods or pauses.

automatic-speech-recognition Speech Recognition

Enhanced 3D convolutional networks for crowd counting

no code implementations12 Aug 2019 Zhikang Zou, Huiliang Shao, Xiaoye Qu, Wei Wei, Pan Zhou

Recently, convolutional neural networks (CNNs) are the leading defacto method for crowd counting.

Crowd Counting

Exact Recovery of Tensor Robust Principal Component Analysis under Linear Transforms

no code implementations16 Jul 2019 Canyi Lu, Pan Zhou

This work studies the Tensor Robust Principal Component Analysis (TRPCA) problem, which aims to exactly recover the low-rank and sparse components from their sum.

EnlightenGAN: Deep Light Enhancement without Paired Supervision

8 code implementations17 Jun 2019 Yifan Jiang, Xinyu Gong, Ding Liu, Yu Cheng, Chen Fang, Xiaohui Shen, Jianchao Yang, Pan Zhou, Zhangyang Wang

Deep learning-based methods have achieved remarkable success in image restoration and enhancement, but are they still competitive when there is a lack of paired training data?

Image Restoration Low-Light Image Enhancement

Adversarial Category Alignment Network for Cross-domain Sentiment Classification

no code implementations NAACL 2019 Xiaoye Qu, Zhikang Zou, Yu Cheng, Yang Yang, Pan Zhou

Cross-domain sentiment classification aims to predict sentiment polarity on a target domain utilizing a classifier learned from a source domain.

Classification General Classification +1

A Stochastic Trust Region Method for Non-convex Minimization

no code implementations ICLR 2020 Zebang Shen, Pan Zhou, Cong Fang, Alejandro Ribeiro

We target the problem of finding a local minimum in non-convex finite-sum minimization.

Gradient Scheduling with Global Momentum for Non-IID Data Distributed Asynchronous Training

no code implementations21 Feb 2019 Chengjie Li, Ruixuan Li, Haozhao Wang, Yuhua Li, Pan Zhou, Song Guo, Keqin Li

Distributed asynchronous offline training has received widespread attention in recent years because of its high performance on large-scale data and complex models.

New Insight into Hybrid Stochastic Gradient Descent: Beyond With-Replacement Sampling and Convexity

no code implementations NeurIPS 2018 Pan Zhou, Xiao-Tong Yuan, Jiashi Feng

In this paper, we affirmatively answer this open question by showing that under WoRS and for both convex and non-convex problems, it is still possible for HSGD (with constant step-size) to match full gradient descent in rate of convergence, while maintaining comparable sample-size-independent incremental first-order oracle complexity to stochastic gradient descent.

Efficient Stochastic Gradient Hard Thresholding

no code implementations NeurIPS 2018 Pan Zhou, Xiao-Tong Yuan, Jiashi Feng

To address these deficiencies, we propose an efficient hybrid stochastic gradient hard thresholding (HSG-HT) method that can be provably shown to have sample-size-independent gradient evaluation and hard thresholding complexity bounds.

Bayesian Cycle-Consistent Generative Adversarial Networks via Marginalizing Latent Sampling

1 code implementation19 Nov 2018 Haoran You, Yu Cheng, Tianheng Cheng, Chunliang Li, Pan Zhou

We evaluate the proposed Bayesian CycleGAN on multiple benchmark datasets, including Cityscapes, Maps, and Monet2photo.

Image-to-Image Translation Semantic Segmentation +1

An Online Attention-based Model for Speech Recognition

no code implementations13 Nov 2018 Ruchao Fan, Pan Zhou, Wei Chen, Jia Jia, Gang Liu

In previous work, researchers have shown that such architectures can acquire comparable results to state-of-the-art ASR systems, especially when using a bidirectional encoder and global soft attention (GSA) mechanism.

automatic-speech-recognition Language Modelling +2

Exploring RNN-Transducer for Chinese Speech Recognition

no code implementations13 Nov 2018 Senmao Wang, Pan Zhou, Wei Chen, Jia Jia, Lei Xie

End-to-end approaches have drawn much attention recently for significantly simplifying the construction of an automatic speech recognition (ASR) system.

automatic-speech-recognition Language Modelling +2

Modality Attention for End-to-End Audio-visual Speech Recognition

no code implementations13 Nov 2018 Pan Zhou, Wenwen Yang, Wei Chen, Yan-Feng Wang, Jia Jia

In this paper, we propose a novel multimodal attention based method for audio-visual speech recognition which could automatically learn the fused representation from both modalities based on their importance.

Audio-Visual Speech Recognition Robust Speech Recognition +1

Spatio-temporal Edge Service Placement: A Bandit Learning Approach

no code implementations7 Oct 2018 Lixing Chen, Jie Xu, Shaolei Ren, Pan Zhou

To solve this problem and optimize the edge computing performance, we propose SEEN, a Spatial-temporal Edge sErvice placemeNt algorithm.

Decision Making Edge-computing

Deep Adversarial Subspace Clustering

no code implementations CVPR 2018 Pan Zhou, Yunqing Hou, Jiashi Feng

To solve this issue, we propose a novel deep adversarial subspace clustering (DASC) model, which learns more favorable sample representations by deep learning for subspace clustering, and more importantly introduces adversarial learning to supervise sample representation learning and subspace clustering.

Image Clustering Representation Learning

Understanding Generalization and Optimization Performance of Deep CNNs

no code implementations ICML 2018 Pan Zhou, Jiashi Feng

Besides, we prove that for an arbitrary gradient descent algorithm, the computed approximate stationary point by minimizing empirical risk is also an approximate stationary point to the population risk.

Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and Beyond

3 code implementations5 Apr 2018 Xi Ouyang, Yu Cheng, Yifan Jiang, Chun-Liang Li, Pan Zhou

The results show that our framework can smoothly synthesize pedestrians on background images of variations and different levels of details.

Pedestrian Detection Scene Text Recognition

Empirical Risk Landscape Analysis for Understanding Deep Neural Networks

no code implementations ICLR 2018 Pan Zhou, Jiashi Feng

This work aims to provide comprehensive landscape analysis of empirical risk in deep neural networks (DNNs), including the convergence behavior of its gradient, its stationary points and the empirical risk itself to their corresponding population counterparts, which reveals how various network parameters determine the convergence performance.

Generalization Bounds

A Survey of Model Compression and Acceleration for Deep Neural Networks

no code implementations23 Oct 2017 Yu Cheng, Duo Wang, Pan Zhou, Tao Zhang

Methods of parameter pruning and quantization are described first, after that the other techniques are introduced.

Knowledge Distillation Model Compression +1

Outlier-Robust Tensor PCA

no code implementations CVPR 2017 Pan Zhou, Jiashi Feng

Low-rank tensor analysis is important for various real applications in computer vision.

Outlier Detection

The Landscape of Deep Learning Algorithms

no code implementations19 May 2017 Pan Zhou, Jiashi Feng

For an $l$-layer linear neural network, we prove its empirical risk uniformly converges to its population risk at the rate of $\mathcal{O}(r^{2l}\sqrt{d\log(l)}/\sqrt{n})$ with training sample size of $n$, the total weight dimension of $d$ and the magnitude bound $r$ of weight of each layer.

Generalization Bounds

Context-Aware Online Learning for Course Recommendation of MOOC Big Data

no code implementations11 Oct 2016 Yifan Hou, Pan Zhou, Ting Wang, Li Yu, Yuchong Hu, Dapeng Wu

In this respect, the key challenge is how to realize personalized course recommendation as well as to reduce the computing and storage costs for the tremendous course data.

Recommendation Systems

Distributed Private Online Learning for Social Big Data Computing over Data Center Networks

no code implementations21 Feb 2016 Chencheng Li, Pan Zhou, Yingxue Zhou, Kaigui Bian, Tao Jiang, Susanto Rahardja

An increasing number of people participate in social networks and massive online social data are obtained.

Differentially Private Online Learning for Cloud-Based Video Recommendation with Multimedia Big Data in Social Networks

no code implementations1 Sep 2015 Pan Zhou, Yingxue Zhou, Dapeng Wu, Hai Jin

In addition, none of them has considered both the privacy of users' contexts (e, g., social status, ages and hobbies) and video service vendors' repositories, which are extremely sensitive and of significant commercial value.

Recommendation Systems

Differentially Private Distributed Online Learning

no code implementations25 May 2015 Chencheng Li, Pan Zhou

Thus, we use differential privacy to preserve the privacy of learners, and study the influence of guaranteeing differential privacy on the utility of the distributed online learning algorithm.

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