Search Results for author: Yuan Jiang

Found 18 papers, 3 papers with code

Learning with Feature and Distribution Evolvable Streams

no code implementations ICML 2020 Zhen-Yu Zhang, Peng Zhao, Yuan Jiang, Zhi-Hua Zhou

Besides the feature space evolving, it is noteworthy that the data distribution often changes in streaming data.

Seeing Differently, Acting Similarly: Imitation Learning with Heterogeneous Observations

no code implementations17 Jun 2021 Xin-Qiang Cai, Yao-Xiang Ding, Zi-Xuan Chen, Yuan Jiang, Masashi Sugiyama, Zhi-Hua Zhou

In many real-world imitation learning tasks, the demonstrator and the learner have to act in different but full observation spaces.

Imitation Learning

Semi-Supervised Multi-Modal Multi-Instance Multi-Label Deep Network with Optimal Transport

no code implementations17 Apr 2021 Yang Yang, Zhao-Yang Fu, De-Chuan Zhan, Zhi-Bin Liu, Yuan Jiang

Moreover, we introduce the extrinsic unlabeled multi-modal multi-instance data, and propose the M3DNS, which considers the instance-level auto-encoder for single modality and modified bag-level optimal transport to strengthen the consistency among modalities.

Voice Conversion by Cascading Automatic Speech Recognition and Text-to-Speech Synthesis with Prosody Transfer

no code implementations3 Sep 2020 Jing-Xuan Zhang, Li-Juan Liu, Yan-Nian Chen, Ya-Jun Hu, Yuan Jiang, Zhen-Hua Ling, Li-Rong Dai

In this paper, we present a ASR-TTS method for voice conversion, which used iFLYTEK ASR engine to transcribe the source speech into text and a Transformer TTS model with WaveNet vocoder to synthesize the converted speech from the decoded text.

automatic-speech-recognition Speech Recognition +3

Provably Robust Metric Learning

1 code implementation NeurIPS 2020 Lu Wang, Xuanqing Liu, Jin-Feng Yi, Yuan Jiang, Cho-Jui Hsieh

Metric learning is an important family of algorithms for classification and similarity search, but the robustness of learned metrics against small adversarial perturbations is less studied.

Metric Learning

Soft Gradient Boosting Machine

no code implementations7 Jun 2020 Ji Feng, Yi-Xuan Xu, Yuan Jiang, Zhi-Hua Zhou

Gradient Boosting Machine has proven to be one successful function approximator and has been widely used in a variety of areas.

Incremental Learning

Spanning Attack: Reinforce Black-box Attacks with Unlabeled Data

1 code implementation11 May 2020 Lu Wang, huan zhang, Jin-Feng Yi, Cho-Jui Hsieh, Yuan Jiang

By constraining adversarial perturbations in a low-dimensional subspace via spanning an auxiliary unlabeled dataset, the spanning attack significantly improves the query efficiency of a wide variety of existing black-box attacks.

Multi-Label Learning with Deep Forest

no code implementations15 Nov 2019 Liang Yang, Xi-Zhu Wu, Yuan Jiang, Zhi-Hua Zhou

In multi-label learning, each instance is associated with multiple labels and the crucial task is how to leverage label correlations in building models.

Multi-Label Learning

Imitation Learning from Pixel-Level Demonstrations by HashReward

no code implementations9 Sep 2019 Xin-Qiang Cai, Yao-Xiang Ding, Yuan Jiang, Zhi-Hua Zhou

One of the key issues for imitation learning lies in making policy learned from limited samples to generalize well in the whole state-action space.

Dimensionality Reduction Imitation Learning

Rectify Heterogeneous Models with Semantic Mapping

no code implementations ICML 2018 Han-Jia Ye, De-Chuan Zhan, Yuan Jiang, Zhi-Hua Zhou

On the way to the robust learner for real-world applications, there are still great challenges, including considering unknown environments with limited data.

Multi-stage Multi-recursive-input Fully Convolutional Networks for Neuronal Boundary Detection

no code implementations ICCV 2017 Wei Shen, Bin Wang, Yuan Jiang, Yan Wang, Alan Yuille

This design is biologically-plausible, as it likes a human visual system to compare different possible segmentation solutions to address the ambiguous boundary issue.

Boundary Detection Electron Microscopy

What Makes Objects Similar: A Unified Multi-Metric Learning Approach

no code implementations NeurIPS 2016 Han-Jia Ye, De-Chuan Zhan, Xue-Min Si, Yuan Jiang, Zhi-Hua Zhou

In UM2L, a type of combination operator is introduced for distance characterization from multiple perspectives, and thus can introduce flexibilities for representing and utilizing both spatial and semantic linkages.

Metric Learning

DeepSkeleton: Learning Multi-task Scale-associated Deep Side Outputs for Object Skeleton Extraction in Natural Images

1 code implementation13 Sep 2016 Wei Shen, Kai Zhao, Yuan Jiang, Yan Wang, Xiang Bai, Alan Yuille

By observing the relationship between the receptive field sizes of the different layers in the network and the skeleton scales they can capture, we introduce two scale-associated side outputs to each stage of the network.

Multi-Task Learning Object Detection +1

Shape Recognition by Bag of Skeleton-associated Contour Parts

no code implementations20 May 2016 Wei Shen, Yuan Jiang, Wenjing Gao, Dan Zeng, Xinggang Wang

Contour and skeleton are two complementary representations for shape recognition.

Object Skeleton Extraction in Natural Images by Fusing Scale-associated Deep Side Outputs

no code implementations CVPR 2016 Wei Shen, Kai Zhao, Yuan Jiang, Yan Wang, Zhijiang Zhang, Xiang Bai

Object skeleton is a useful cue for object detection, complementary to the object contour, as it provides a structural representation to describe the relationship among object parts.

Object Detection

Multi-Label Active Learning from Crowds

no code implementations4 Aug 2015 Shao-Yuan Li, Yuan Jiang, Zhi-Hua Zhou

Multi-label active learning is a hot topic in reducing the label cost by optimally choosing the most valuable instance to query its label from an oracle.

Active Learning

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