Search Results for author: Yuhong Guo

Found 43 papers, 5 papers with code

Safe Reinforcement Learning with Contrastive Risk Prediction

no code implementations10 Sep 2022 Hanping Zhang, Yuhong Guo

As safety violations can lead to severe consequences in real-world robotic applications, the increasing deployment of Reinforcement Learning (RL) in robotic domains has propelled the study of safe exploration for reinforcement learning (safe RL).

reinforcement-learning Safe Exploration +1

Exemplar Learning for Medical Image Segmentation

no code implementations3 Apr 2022 Qing En, Yuhong Guo

To address this challenging EL task, we propose an Exemplar Learning-based Synthesis Net (ELSNet) framework for medical image segmentation that enables innovative exemplar-based data synthesis, pixel-prototype based contrastive embedding learning, and pseudo-label based exploitation of the unlabeled data.

Image Segmentation Medical Image Segmentation +3

Contrastive Continual Learning with Feature Propagation

no code implementations3 Dec 2021 Xuejun Han, Yuhong Guo

To address this shortcoming, continual machine learners are elaborated to commendably learn a stream of tasks with domain and class shifts among different tasks.

Continual Learning Representation Learning

Dual GNNs: Graph Neural Network Learning with Limited Supervision

no code implementations29 Jun 2021 Abdullah Alchihabi, Yuhong Guo

In this paper, we propose a novel Dual GNN learning framework to address this challenge task.

Node Classification

Generalization of Reinforcement Learning with Policy-Aware Adversarial Data Augmentation

no code implementations29 Jun 2021 Hanping Zhang, Yuhong Guo

In this work, we propose a novel policy-aware adversarial data augmentation method to augment the standard policy learning method with automatically generated trajectory data.

Data Augmentation reinforcement-learning

Selective Pseudo-Labeling with Reinforcement Learning for Semi-Supervised Domain Adaptation

no code implementations7 Dec 2020 Bingyu Liu, Yuhong Guo, Jieping Ye, Weihong Deng

Inspired by the effectiveness of pseudo-labels in domain adaptation, we propose a reinforcement learning based selective pseudo-labeling method for semi-supervised domain adaptation.

Q-Learning reinforcement-learning +1

Domain Adaptation with Incomplete Target Domains

no code implementations3 Dec 2020 Zhenpeng Li, Jianan Jiang, Yuhong Guo, Tiantian Tang, Chengxiang Zhuo, Jieping Ye

In the proposed model, we design a data imputation module to fill the missing feature values based on the partial observations in the target domain, while aligning the two domains via deep adversarial adaption.

Domain Adaptation Imputation

A Transductive Multi-Head Model for Cross-Domain Few-Shot Learning

1 code implementation8 Jun 2020 Jianan Jiang, Zhenpeng Li, Yuhong Guo, Jieping Ye

The TMHFS method extends the Meta-Confidence Transduction (MCT) and Dense Feature-Matching Networks (DFMN) method [2] by introducing a new prediction head, i. e, an instance-wise global classification network based on semantic information, after the common feature embedding network.

cross-domain few-shot learning Data Augmentation

Ensemble Model with Batch Spectral Regularization and Data Blending for Cross-Domain Few-Shot Learning with Unlabeled Data

no code implementations8 Jun 2020 Zhen Zhao, Bingyu Liu, Yuhong Guo, Jieping Ye

In this paper, we present our proposed ensemble model with batch spectral regularization and data blending mechanisms for the Track 2 problem of the cross-domain few-shot learning (CD-FSL) challenge.

cross-domain few-shot learning

Feature Transformation Ensemble Model with Batch Spectral Regularization for Cross-Domain Few-Shot Classification

no code implementations18 May 2020 Bingyu Liu, Zhen Zhao, Zhenpeng Li, Jianan Jiang, Yuhong Guo, Jieping Ye

In this paper, we propose a feature transformation ensemble model with batch spectral regularization for the Cross-domain few-shot learning (CD-FSL) challenge.

cross-domain few-shot learning Data Augmentation +2

Multi-Level Generative Models for Partial Label Learning with Non-random Label Noise

no code implementations11 May 2020 Yan Yan, Yuhong Guo

Partial label (PL) learning tackles the problem where each training instance is associated with a set of candidate labels that include both the true label and irrelevant noise labels.

Denoising Partial Label Learning

Unsupervised Domain Adaptation with Progressive Domain Augmentation

no code implementations3 Apr 2020 Kevin Hua, Yuhong Guo

Domain adaptation aims to exploit a label-rich source domain for learning classifiers in a different label-scarce target domain.

Unsupervised Domain Adaptation

Mutual Learning Network for Multi-Source Domain Adaptation

no code implementations29 Mar 2020 Zhenpeng Li, Zhen Zhao, Yuhong Guo, Haifeng Shen, Jieping Ye

However, in practice the labeled data can come from multiple source domains with different distributions.

Unsupervised Domain Adaptation

Adaptive Object Detection with Dual Multi-Label Prediction

no code implementations ECCV 2020 Zhen Zhao, Yuhong Guo, Haifeng Shen, Jieping Ye

In this paper, we propose a novel end-to-end unsupervised deep domain adaptation model for adaptive object detection by exploiting multi-label object recognition as a dual auxiliary task.

Image-to-Image Translation object-detection +3

Time-aware Large Kernel Convolutions

1 code implementation ICML 2020 Vasileios Lioutas, Yuhong Guo

Some of these models use all the available sequence tokens to generate an attention distribution which results in time complexity of $O(n^2)$.

Document Summarization Language Modelling +2

Adversarial Paritial Multi-label Learning

no code implementations ICLR 2020 Yan Yan, Yuhong Guo

Partial multi-label learning (PML), which tackles the problem of learning multi-label prediction models from instances with overcomplete noisy annotations, has recently started gaining attention from the research community.

Multi-Label Learning

Dual Adversarial Co-Learning for Multi-Domain Text Classification

no code implementations18 Sep 2019 Yuan Wu, Yuhong Guo

In this paper we propose a novel dual adversarial co-learning approach for multi-domain text classification (MDTC).

Classification General Classification +4

Learning to Generate Questions with Adaptive Copying Neural Networks

no code implementations17 Sep 2019 Xinyuan Lu, Yuhong Guo

Automatic question generation is an important problem in natural language processing.

Question Generation

Inverse Visual Question Answering with Multi-Level Attentions

no code implementations17 Sep 2019 Yaser Alwattar, Yuhong Guo

In this paper, we propose a novel deep multi-level attention model to address inverse visual question answering.

Question Answering Visual Question Answering +1

Adversarial Partial Multi-Label Learning

no code implementations15 Sep 2019 Yan Yan, Yuhong Guo

Partial multi-label learning (PML), which tackles the problem of learning multi-label prediction models from instances with overcomplete noisy annotations, has recently started gaining attention from the research community.

Multi-Label Learning

Object Detection in 20 Years: A Survey

1 code implementation13 May 2019 Zhengxia Zou, Zhenwei Shi, Yuhong Guo, Jieping Ye

Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years.

Face Detection object-detection +2

Multi-Label Zero-Shot Learning with Transfer-Aware Label Embedding Projection

no code implementations7 Aug 2018 Meng Ye, Yuhong Guo

The approach projects the label embedding vectors into a low-dimensional space to induce better inter-label relationships and explicitly facilitate information transfer from seen labels to unseen labels, while simultaneously learning a max-margin multi-label classifier with the projected label embeddings.

Multi-Label Image Classification Multi-label zero-shot learning +1

Progressive Ensemble Networks for Zero-Shot Recognition

no code implementations CVPR 2019 Meng Ye, Yuhong Guo

The ensemble network is built by learning multiple image classification functions with a shared feature extraction network but different label embedding representations, which enhance the diversity of the classifiers and facilitate information transfer to unlabeled classes.

Generalized Zero-Shot Learning Image Classification

Visual relationship detection with deep structural ranking

1 code implementation27 Apr 2018 Kongming Liang, Yuhong Guo, Hong Chang, Xilin Chen

In this paper, we propose a novel framework, called Deep Structural Ranking, for visual relationship detection.

Visual Relationship Detection

Deep Triplet Ranking Networks for One-Shot Recognition

1 code implementation19 Apr 2018 Meng Ye, Yuhong Guo

Despite the breakthroughs achieved by deep learning models in conventional supervised learning scenarios, their dependence on sufficient labeled training data in each class prevents effective applications of these deep models in situations where labeled training instances for a subset of novel classes are very sparse -- in the extreme case only one instance is available for each class.

One-Shot Learning

Zero-Shot Classification With Discriminative Semantic Representation Learning

no code implementations CVPR 2017 Meng Ye, Yuhong Guo

The proposed approach aims to identify a set of common high-level semantic components across the two domains via non-negative sparse matrix factorization, while enforcing the representation vectors of the images in this common component-based space to be discriminatively aligned with the attribute-based label representation vectors.

Classification General Classification +3

Semi-Supervised Zero-Shot Classification With Label Representation Learning

no code implementations ICCV 2015 Xin Li, Yuhong Guo, Dale Schuurmans

Most existing zero-shot learning methods require a user to first provide a set of semantic visual attributes for each class as side information before applying a two-step prediction procedure that introduces an intermediate attribute prediction problem.

Classification General Classification +3

A Novel Two-Step Method for Cross Language Representation Learning

no code implementations NeurIPS 2013 Min Xiao, Yuhong Guo

In this paper, we propose a two-step representation learning method to bridge the feature spaces of different languages by exploiting a set of parallel bilingual documents.

Matrix Completion Representation Learning

Robust Transfer Principal Component Analysis with Rank Constraints

no code implementations NeurIPS 2013 Yuhong Guo

Our method is based on the assumption that useful information for the recovery of a corrupted data matrix can be gained from an uncorrupted related data matrix.

Dimensionality Reduction Image Denoising

Adaptive Active Learning for Image Classification

no code implementations CVPR 2013 Xin Li, Yuhong Guo

Recently active learning has attracted a lot of attention in computer vision field, as it is time and cost consuming to prepare a good set of labeled images for vision data analysis.

Active Learning Classification +4

Supervised Exponential Family Principal Component Analysis via Convex Optimization

no code implementations NeurIPS 2008 Yuhong Guo

Recently, supervised dimensionality reduction has been gaining attention, owing to the realization that data labels are often available and strongly suggest important underlying structures in the data.

Supervised dimensionality reduction

Discriminative Batch Mode Active Learning

no code implementations NeurIPS 2007 Yuhong Guo, Dale Schuurmans

Most previous studies in active learning have focused on selecting one unlabeled instance at one time while retraining in each iteration.

Active Learning

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