Search Results for author: Ivor Tsang

Found 22 papers, 4 papers with code

Imitation Learning: Progress, Taxonomies and Opportunities

no code implementations23 Jun 2021 Boyuan Zheng, Sunny Verma, Jianlong Zhou, Ivor Tsang, Fang Chen

Imitation learning aims to extract knowledge from human experts' demonstrations or artificially created agents in order to replicate their behaviors.

Autonomous Driving Imitation Learning

Neural Optimization Kernel: Towards Robust Deep Learning

no code implementations11 Jun 2021 Yueming Lyu, Ivor Tsang

Moreover, we propose a data-dependent structured approximation of our NOK, which builds the connection between training deep NNs and kernel methods associated with NOK.

Generalization Bounds

Contrastive Attraction and Contrastive Repulsion for Representation Learning

no code implementations8 May 2021 Huangjie Zheng, Xu Chen, Jiangchao Yao, Hongxia Yang, Chunyuan Li, Ya zhang, Hao Zhang, Ivor Tsang, Jingren Zhou, Mingyuan Zhou

Extensive large-scale experiments on standard vision tasks show that CACR not only consistently outperforms existing CL methods on benchmark datasets in representation learning, but also provides interpretable contrastive weights, demonstrating the efficacy of the proposed doubly contrastive strategy.

Contrastive Learning Representation Learning

Generative Transition Mechanism to Image-to-Image Translation via Encoded Transformation

no code implementations9 Mar 2021 Yaxin Shi, Xiaowei Zhou, Ping Liu, Ivor Tsang

To benefit the generalization ability of the translation model, we propose transition encoding to facilitate explicit regularization of these two {kinds} of consistencies on unseen transitions.

Image Reconstruction Image-to-Image Translation

Human-Understandable Decision Making for Visual Recognition

no code implementations5 Mar 2021 Xiaowei Zhou, Jie Yin, Ivor Tsang, Chen Wang

The widespread use of deep neural networks has achieved substantial success in many tasks.

Decision Making

On the Geometry of Deep Bayesian Active Learning

no code implementations1 Jan 2021 Xiaofeng Cao, Ivor Tsang

To guarantee the improvements, our generalization analysis proves that, compared to typical Bayesian spherical interpretation, geodesic search with ellipsoid can derive a tighter lower error bound and achieve higher probability to obtain a nearly zero error.

Active Learning

A Simple Sparse Denoising Layer for Robust Deep Learning

no code implementations1 Jan 2021 Yueming Lyu, Xingrui Yu, Ivor Tsang

In this work, we take an initial step to designing a simple robust layer as a lightweight plug-in for vanilla deep models.

Denoising Dictionary Learning

Streamlining EM into Auto-Encoder Networks

no code implementations1 Jan 2021 Yuangang Pan, Ivor Tsang

We present a new deep neural network architecture, named EDGaM, for deep clustering.

Deep Clustering

TRIP: Refining Image-to-Image Translation via Rival Preferences

no code implementations1 Jan 2021 Yinghua Yao, Yuangang Pan, Ivor Tsang, Xin Yao

In particular, we simultaneously train two modules: a generator that translates an input image to the desired image with smooth subtle changes with respect to some specific attributes; and a ranker that ranks rival preferences consisting of the input image and the desired image.

Image-to-Image Translation

Learning Efficient Planning-based Rewards for Imitation Learning

no code implementations1 Jan 2021 Xingrui Yu, Yueming Lyu, Ivor Tsang

Our method learns useful planning computations with a meaningful reward function that focuses on the resulting region of an agent executing an action.

Atari Games Continuous Control +1

Towards Equivalent Transformation of User Preferences in Cross Domain Recommendation

1 code implementation15 Sep 2020 Xu Chen, Ya zhang, Ivor Tsang, Yuangang Pan, Jingchao Su

The majority of recent methods have explored shared-user representation to transfer knowledge across different domains.

Recommendation Systems

Learning Robust Node Representations on Graphs

no code implementations26 Aug 2020 Xu Chen, Ya zhang, Ivor Tsang, Yuangang Pan

Graph neural networks (GNN), as a popular methodology for node representation learning on graphs, currently mainly focus on preserving the smoothness and identifiability of node representations.

Contrastive Learning Representation Learning

Copy and Paste GAN: Face Hallucination from Shaded Thumbnails

no code implementations CVPR 2020 Yang Zhang, Ivor Tsang, Yawei Luo, Changhui Hu, Xiaobo Lu, Xin Yu

This paper proposes a Copy and Paste Generative Adversarial Network (CPGAN) to recover authentic high-resolution (HR) face images while compensating for low and non-uniform illumination.

Face Hallucination

Domain-adversarial Network Alignment

no code implementations15 Aug 2019 Huiting Hong, Xin Li, Yuangang Pan, Ivor Tsang

Network alignment is a critical task to a wide variety of fields.

Network Embedding

Probabilistic CCA with Implicit Distributions

no code implementations4 Jul 2019 Yaxin Shi, Yuangang Pan, Donna Xu, Ivor Tsang

Although some works have studied probabilistic interpretation for CCA, these models still require the explicit form of the distributions to achieve a tractable solution for the inference.

Bayesian Inference MULTI-VIEW LEARNING

SIGUA: Forgetting May Make Learning with Noisy Labels More Robust

1 code implementation ICML 2020 Bo Han, Gang Niu, Xingrui Yu, Quanming Yao, Miao Xu, Ivor Tsang, Masashi Sugiyama

Given data with noisy labels, over-parameterized deep networks can gradually memorize the data, and fit everything in the end.

Learning with noisy labels

Masking: A New Perspective of Noisy Supervision

2 code implementations NeurIPS 2018 Bo Han, Jiangchao Yao, Gang Niu, Mingyuan Zhou, Ivor Tsang, Ya zhang, Masashi Sugiyama

It is important to learn various types of classifiers given training data with noisy labels.

Ranked #28 on Image Classification on Clothing1M (using extra training data)

Image Classification

Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels

4 code implementations NeurIPS 2018 Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, Masashi Sugiyama

Deep learning with noisy labels is practically challenging, as the capacity of deep models is so high that they can totally memorize these noisy labels sooner or later during training.

Learning with noisy labels

Variational Composite Autoencoders

no code implementations12 Apr 2018 Jiangchao Yao, Ivor Tsang, Ya zhang

Learning in the latent variable model is challenging in the presence of the complex data structure or the intractable latent variable.

Sparse Embedded k-Means Clustering

no code implementations NeurIPS 2017 Weiwei Liu, Xiaobo Shen, Ivor Tsang

For example, compared to the advanced singular value decomposition based feature extraction approach, [1] reduce the running time by a factor of $\min \{n, d\}\epsilon^2 log(d)/k$ for data matrix $X \in \mathbb{R}^{n\times d} $ with $n$ data points and $d$ features, while losing only a factor of one in approximation accuracy.

Dimensionality Reduction

Deep Learning from Noisy Image Labels with Quality Embedding

no code implementations2 Nov 2017 Jiangchao Yao, Jiajie Wang, Ivor Tsang, Ya zhang, Jun Sun, Chengqi Zhang, Rui Zhang

However, the label noise among the datasets severely degenerates the \mbox{performance of deep} learning approaches.

On the Optimality of Classifier Chain for Multi-label Classification

no code implementations NeurIPS 2015 Weiwei Liu, Ivor Tsang

Based on our results, we propose a dynamic programming based classifier chain (CC-DP) algorithm to search the globally optimal label order for CC and a greedy classifier chain (CC-Greedy) algorithm to find a locally optimal CC.

Classification General Classification +1

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