Search Results for author: Jian-Min Wang

Found 45 papers, 20 papers with code

Unsupervised Transfer Learning for Spatiotemporal Predictive Networks

1 code implementation ICML 2020 Zhiyu Yao, Yunbo Wang, Mingsheng Long, Jian-Min Wang

This paper explores a new research problem of unsupervised transfer learning across multiple spatiotemporal prediction tasks.

Transfer Learning

On Localized Discrepancy for Domain Adaptation

no code implementations14 Aug 2020 Yuchen Zhang, Mingsheng Long, Jian-Min Wang, Michael. I. Jordan

Finally, we further extend the localized discrepancies for achieving super transfer and derive generalization bounds that could be even more sample-efficient on source domain.

Generalization Bounds Unsupervised Domain Adaptation

Transferable Calibration with Lower Bias and Variance in Domain Adaptation

no code implementations NeurIPS 2020 Ximei Wang, Mingsheng Long, Jian-Min Wang, Michael. I. Jordan

In this paper, we delve into the open problem of Calibration in DA, which is extremely challenging due to the coexistence of domain shift and the lack of target labels.

Decision Making Domain Adaptation

Learning Individual Models for Imputation (Technical Report)

no code implementations7 Apr 2020 Aoqian Zhang, Shaoxu Song, Yu Sun, Jian-Min Wang

We propose to adaptively learn individual models over various number l of neighbors for different complete tuples.

Imputation

Minimum Class Confusion for Versatile Domain Adaptation

2 code implementations ECCV 2020 Ying Jin, Ximei Wang, Mingsheng Long, Jian-Min Wang

It can be characterized as (1) a non-adversarial DA method without explicitly deploying domain alignment, enjoying faster convergence speed; (2) a versatile approach that can handle four existing scenarios: Closed-Set, Partial-Set, Multi-Source, and Multi-Target DA, outperforming the state-of-the-art methods in these scenarios, especially on one of the largest and hardest datasets to date (7. 3% on DomainNet).

Multi-target Domain Adaptation Partial Domain Adaptation

Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer Learning

2 code implementations NeurIPS 2019 Xinyang Chen, Sinan Wang, Bo Fu, Mingsheng Long, Jian-Min Wang

Before sufficient training data is available, fine-tuning neural networks pre-trained on large-scale datasets substantially outperforms training from random initialization.

Transfer Learning

Towards Understanding the Transferability of Deep Representations

no code implementations26 Sep 2019 Hong Liu, Mingsheng Long, Jian-Min Wang, Michael. I. Jordan

3) The feasibility of transferability is related to the similarity of both input and label.

How Does Learning Rate Decay Help Modern Neural Networks?

no code implementations ICLR 2020 Kaichao You, Mingsheng Long, Jian-Min Wang, Michael. I. Jordan

Despite the popularity of these common beliefs, experiments suggest that they are insufficient in explaining the general effectiveness of lrDecay in training modern neural networks that are deep, wide, and nonconvex.

An Approach for Process Model Extraction By Multi-Grained Text Classification

1 code implementation16 May 2019 Chen Qian, Lijie Wen, Akhil Kumar, Leilei Lin, Li Lin, Zan Zong, Shuang Li, Jian-Min Wang

Process model extraction (PME) is a recently emerged interdiscipline between natural language processing (NLP) and business process management (BPM), which aims to extract process models from textual descriptions.

Classification General Classification +4

Inpatient2Vec: Medical Representation Learning for Inpatients

no code implementations18 Apr 2019 Ying Wang, Xiao Xu, Tao Jin, Xiang Li, Guotong Xie, Jian-Min Wang

In addition, for unordered medical activity set, existing medical RL methods utilize a simple pooling strategy, which would result in indistinguishable contributions among the activities for learning.

Representation Learning Semantic Similarity +1

Learning to Transfer Examples for Partial Domain Adaptation

1 code implementation CVPR 2019 Zhangjie Cao, Kaichao You, Mingsheng Long, Jian-Min Wang, Qiang Yang

Under the condition that target labels are unknown, the key challenge of PDA is how to transfer relevant examples in the shared classes to promote positive transfer, and ignore irrelevant ones in the specific classes to mitigate negative transfer.

Partial Domain Adaptation Transfer Learning

Spatiotemporal Pyramid Network for Video Action Recognition

no code implementations CVPR 2017 Yunbo Wang, Mingsheng Long, Jian-Min Wang, Philip S. Yu

From the technical perspective, we introduce the spatiotemporal compact bilinear operator into video analysis tasks.

Action Recognition

Deep Triplet Quantization

1 code implementation1 Feb 2019 Bin Liu, Yue Cao, Mingsheng Long, Jian-Min Wang, Jingdong Wang

We propose Deep Triplet Quantization (DTQ), a novel approach to learning deep quantization models from the similarity triplets.

Image Retrieval Quantization

Flexible Attributed Network Embedding

no code implementations27 Nov 2018 Enya Shen, Zhidong Cao, Changqing Zou, Jian-Min Wang

In this paper, we propose a novel framework, FANE, to integrate structure and property information in the network embedding process.

General Classification Network Embedding

Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics

2 code implementations CVPR 2019 Yunbo Wang, Jianjin Zhang, Hongyu Zhu, Mingsheng Long, Jian-Min Wang, Philip S. Yu

Natural spatiotemporal processes can be highly non-stationary in many ways, e. g. the low-level non-stationarity such as spatial correlations or temporal dependencies of local pixel values; and the high-level variations such as the accumulation, deformation or dissipation of radar echoes in precipitation forecasting.

Time Series Time Series Forecasting +1

Supermassive Black Holes with High Accretion Rates in Active Galactic Nuclei. VIII. Structure of the Broad-Line Region and Mass of the Central Black Hole in Mrk 142

no code implementations15 Nov 2018 Yan-Rong Li, Yu-Yang Songsheng, Jie Qiu, Chen Hu, Pu Du, Kai-Xing Lu, Ying-Ke Huang, Jin-Ming Bai, Wei-Hao Bian, Ye-Fei Yuan, Luis C. Ho, Jian-Min Wang

We apply three BLR models with different prescriptions of BLR clouds distributions and find that the best model for fitting the data of Mrk 142 is a two-zone BLR model, consistent with the theoretical BLR model surrounding slim accretion disks.

Astrophysics of Galaxies Instrumentation and Methods for Astrophysics

Multi-Adversarial Domain Adaptation

2 code implementations4 Sep 2018 Zhongyi Pei, Zhangjie Cao, Mingsheng Long, Jian-Min Wang

Recent advances in deep domain adaptation reveal that adversarial learning can be embedded into deep networks to learn transferable features that reduce distribution discrepancy between the source and target domains.

Domain Adaptation

Deep Priority Hashing

1 code implementation4 Sep 2018 Zhangjie Cao, Ziping Sun, Mingsheng Long, Jian-Min Wang, Philip S. Yu

Deep hashing enables image retrieval by end-to-end learning of deep representations and hash codes from training data with pairwise similarity information.

Image Retrieval Quantization

Cross-Modal Hamming Hashing

no code implementations ECCV 2018 Yue Cao , Bin Liu, Mingsheng Long, Jian-Min Wang

Extensive experiments demonstrate that CMHH can generate highly concentrated hash codes and achieve state-of-the-art cross-modal retrieval performance for both hash lookups and linear scan scenarios on three benchmark datasets, NUS-WIDE, MIRFlickr-25K, and IAPR TC-12.

Cross-Modal Retrieval

Partial Adversarial Domain Adaptation

2 code implementations ECCV 2018 Zhangjie Cao, Lijia Ma, Mingsheng Long, Jian-Min Wang

We present Partial Adversarial Domain Adaptation (PADA), which simultaneously alleviates negative transfer by down-weighing the data of outlier source classes for training both source classifier and domain adversary, and promotes positive transfer by matching the feature distributions in the shared label space.

Partial Domain Adaptation

Deep Cauchy Hashing for Hamming Space Retrieval

no code implementations CVPR 2018 Yue Cao, Mingsheng Long, Bin Liu, Jian-Min Wang

Due to its computation efficiency and retrieval quality, hashing has been widely applied to approximate nearest neighbor search for large-scale image retrieval, while deep hashing further improves the retrieval quality by end-to-end representation learning and hash coding.

Image Retrieval Representation Learning

HashGAN: Deep Learning to Hash With Pair Conditional Wasserstein GAN

no code implementations CVPR 2018 Yue Cao, Bin Liu, Mingsheng Long, Jian-Min Wang

The main idea is to augment the training data with nearly real images synthesized from a new Pair Conditional Wasserstein GAN (PC-WGAN) conditioned on the pairwise similarity information.

Image Retrieval Representation Learning

Transfer Adversarial Hashing for Hamming Space Retrieval

no code implementations13 Dec 2017 Zhangjie Cao, Mingsheng Long, Chao Huang, Jian-Min Wang

Existing work on deep hashing assumes that the database in the target domain is identically distributed with the training set in the source domain.

Image Retrieval

PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs

no code implementations NeurIPS 2017 Yunbo Wang, Mingsheng Long, Jian-Min Wang, Zhifeng Gao, Philip S. Yu

The core of this network is a new Spatiotemporal LSTM (ST-LSTM) unit that extracts and memorizes spatial and temporal representations simultaneously.

Video Prediction

Fine-grained Pattern Matching Over Streaming Time Series

no code implementations27 Oct 2017 Rong Kang, Chen Wang, Peng Wang, Yuting Ding, Jian-Min Wang

Hence, we formulate a new problem, called "fine-grained pattern matching", which allows users to specify varied granularities of matching deviation to different segments of a given pattern, and fuzzy regions for adaptive breakpoints determination between consecutive segments.

Time Series

Deep Visual-Semantic Quantization for Efficient Image Retrieval

no code implementations CVPR 2017 Yue Cao, Mingsheng Long, Jian-Min Wang, Shichen Liu

This paper presents a compact coding solution with a focus on the deep learning to quantization approach, which improves retrieval quality by end-to-end representation learning and compact encoding and has already shown the superior performance over the hashing solutions for similarity retrieval.

Image Retrieval Quantization +1

Time Series Data Cleaning: From Anomaly Detection to Anomaly Repairing

1 code implementation Proceedings of the VLDB Endowment 2017 Aoqian Zhang, Shaoxu Song, Jian-Min Wang, Philip S. Yu

Instead of simply discarding anomalies, we propose to (iteratively) repair them in time series data, by creatively bonding the beauty of temporal nature in anomaly detection with the widely considered minimum change principle in data repairing.

Anomaly Detection Time Series +1

Conditional Adversarial Domain Adaptation

3 code implementations NeurIPS 2018 Mingsheng Long, Zhangjie Cao, Jian-Min Wang, Michael. I. Jordan

Adversarial learning has been embedded into deep networks to learn disentangled and transferable representations for domain adaptation.

Domain Adaptation General Classification

HashNet: Deep Learning to Hash by Continuation

1 code implementation ICCV 2017 Zhangjie Cao, Mingsheng Long, Jian-Min Wang, Philip S. Yu

Learning to hash has been widely applied to approximate nearest neighbor search for large-scale multimedia retrieval, due to its computation efficiency and retrieval quality.

Binarization Representation Learning

Correlation Hashing Network for Efficient Cross-Modal Retrieval

no code implementations22 Feb 2016 Yue Cao, Mingsheng Long, Jian-Min Wang, Philip S. Yu

This paper presents a Correlation Hashing Network (CHN) approach to cross-modal hashing, which jointly learns good data representation tailored to hash coding and formally controls the quantization error.

Cross-Modal Retrieval Quantization

Unsupervised Domain Adaptation with Residual Transfer Networks

1 code implementation NeurIPS 2016 Mingsheng Long, Han Zhu, Jian-Min Wang, Michael. I. Jordan

In this paper, we propose a new approach to domain adaptation in deep networks that can jointly learn adaptive classifiers and transferable features from labeled data in the source domain and unlabeled data in the target domain.

Unsupervised Domain Adaptation

Learning Multiple Tasks with Multilinear Relationship Networks

no code implementations NeurIPS 2017 Mingsheng Long, Zhangjie Cao, Jian-Min Wang, Philip S. Yu

Deep networks trained on large-scale data can learn transferable features to promote learning multiple tasks.

Multi-Task Learning

Semantics-Preserving Hashing for Cross-View Retrieval

no code implementations CVPR 2015 Zijia Lin, Guiguang Ding, Mingqing Hu, Jian-Min Wang

With benefits of low storage costs and high query speeds, hashing methods are widely researched for efficiently retrieving large-scale data, which commonly contains multiple views, e. g. a news report with images, videos and texts.

Learning Transferable Features with Deep Adaptation Networks

4 code implementations10 Feb 2015 Mingsheng Long, Yue Cao, Jian-Min Wang, Michael. I. Jordan

Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks for domain adaptation.

Domain Adaptation Image Classification

Transfer Joint Matching for Unsupervised Domain Adaptation

no code implementations CVPR 2014 Mingsheng Long, Jian-Min Wang, Guiguang Ding, Jiaguang Sun, Philip S. Yu

Visual domain adaptation, which learns an accurate classifier for a new domain using labeled images from an old domain, has shown promising value in computer vision yet still been a challenging problem.

Dimensionality Reduction Unsupervised Domain Adaptation

Transfer Sparse Coding for Robust Image Representation

no code implementations CVPR 2013 Mingsheng Long, Guiguang Ding, Jian-Min Wang, Jiaguang Sun, Yuchen Guo, Philip S. Yu

In this paper, we propose a Transfer Sparse Coding (TSC) approach to construct robust sparse representations for classifying cross-distribution images accurately.

Image Tag Completion via Image-Specific and Tag-Specific Linear Sparse Reconstructions

no code implementations CVPR 2013 Zijia Lin, Guiguang Ding, Mingqing Hu, Jian-Min Wang, Xiaojun Ye

Though widely utilized for facilitating image management, user-provided image tags are usually incomplete and insufficient to describe the whole semantic content of corresponding images, resulting in performance degradations in tag-dependent applications and thus necessitating effective tag completion methods.

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