Search Results for author: Xingchao Peng

Found 16 papers, 3 papers with code

Network Architecture Search for Domain Adaptation

no code implementations13 Aug 2020 Yichen Li, Xingchao Peng

Deep networks have been used to learn transferable representations for domain adaptation.

Domain Adaptation Image Classification +1

Domain2Vec: Domain Embedding for Unsupervised Domain Adaptation

no code implementations ECCV 2020 Xingchao Peng, Yichen Li, Kate Saenko

Extensive experiments are conducted to demonstrate the power of our new datasets in benchmarking state-of-the-art multi-source domain adaptation methods, as well as the advantage of our proposed model.

Disentanglement Transfer Learning +1

Learning Domain Adaptive Features with Unlabeled Domain Bridges

no code implementations10 Dec 2019 Yichen Li, Xingchao Peng

Secondly, we propose the Prototypical Adversarial Domain Adaptation (PADA) model which utilizes unlabeled bridge domains to align feature distribution between source and target with a large discrepancy.

Image-to-Image Translation Translation +1

Federated Adversarial Domain Adaptation

no code implementations ICLR 2020 Xingchao Peng, Zijun Huang, Yizhe Zhu, Kate Saenko

In this work, we present a principled approach to the problem of federated domain adaptation, which aims to align the representations learned among the different nodes with the data distribution of the target node.

Disentanglement Domain Adaptation +3

Generalized Domain Adaptation with Covariate and Label Shift CO-ALignment

no code implementations25 Sep 2019 Shuhan Tan, Xingchao Peng, Kate Saenko

In this paper, we explore the task of Generalized Domain Adaptation (GDA): How to transfer knowledge across different domains in the presence of both covariate and label shift?

Domain Adaptation Transfer Learning

Domain Agnostic Learning with Disentangled Representations

1 code implementation28 Apr 2019 Xingchao Peng, Zijun Huang, Ximeng Sun, Kate Saenko

Unsupervised model transfer has the potential to greatly improve the generalizability of deep models to novel domains.

General Classification Image Classification +1

Adapting control policies from simulation to reality using a pairwise loss

no code implementations27 Jul 2018 Ulrich Viereck, Xingchao Peng, Kate Saenko, Robert Platt

This paper proposes an approach to domain transfer based on a pairwise loss function that helps transfer control policies learned in simulation onto a real robot.

Syn2Real: A New Benchmark forSynthetic-to-Real Visual Domain Adaptation

no code implementations26 Jun 2018 Xingchao Peng, Ben Usman, Kuniaki Saito, Neela Kaushik, Judy Hoffman, Kate Saenko

In this paper, we present a new large-scale benchmark called Syn2Real, which consists of a synthetic domain rendered from 3D object models and two real-image domains containing the same object categories.

Classification Domain Adaptation +3

VisDA: The Visual Domain Adaptation Challenge

1 code implementation18 Oct 2017 Xingchao Peng, Ben Usman, Neela Kaushik, Judy Hoffman, Dequan Wang, Kate Saenko

We present the 2017 Visual Domain Adaptation (VisDA) dataset and challenge, a large-scale testbed for unsupervised domain adaptation across visual domains.

General Classification Image Classification +2

Synthetic to Real Adaptation with Generative Correlation Alignment Networks

no code implementations19 Jan 2017 Xingchao Peng, Kate Saenko

Experimentally, we show training off-the-shelf classifiers on the newly generated data can significantly boost performance when testing on the real image domains (PASCAL VOC 2007 benchmark and Office dataset), improving upon several existing methods.

Domain Adaptation Object Recognition

Combining Texture and Shape Cues for Object Recognition With Minimal Supervision

no code implementations14 Sep 2016 Xingchao Peng, Kate Saenko

We present a novel approach to object classification and detection which requires minimal supervision and which combines visual texture cues and shape information learned from freely available unlabeled web search results.

General Classification Image Retrieval +1

Fine-to-coarse Knowledge Transfer For Low-Res Image Classification

no code implementations21 May 2016 Xingchao Peng, Judy Hoffman, Stella X. Yu, Kate Saenko

We address the difficult problem of distinguishing fine-grained object categories in low resolution images.

Classification General Classification +2

What Do Deep CNNs Learn About Objects?

no code implementations9 Apr 2015 Xingchao Peng, Baochen Sun, Karim Ali, Kate Saenko

Deep convolutional neural networks learn extremely powerful image representations, yet most of that power is hidden in the millions of deep-layer parameters.

Learning Deep Object Detectors from 3D Models

no code implementations ICCV 2015 Xingchao Peng, Baochen Sun, Karim Ali, Kate Saenko

Crowdsourced 3D CAD models are becoming easily accessible online, and can potentially generate an infinite number of training images for almost any object category. We show that augmenting the training data of contemporary Deep Convolutional Neural Net (DCNN) models with such synthetic data can be effective, especially when real training data is limited or not well matched to the target domain.

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