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Domain Adaptation

390 papers with code · Methodology

Domain adaptation is the task of adapting models between domains.

( Image credit: Unsupervised Image-to-Image Translation Networks )

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Greatest papers with code

Deep Residual Learning for Image Recognition

CVPR 2016 tensorflow/models

Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

DOMAIN GENERALIZATION FINE-GRAINED IMAGE CLASSIFICATION IMAGE-TO-IMAGE TRANSLATION MULTI-LABEL CLASSIFICATION OBJECT DETECTION PERSON RE-IDENTIFICATION SEMANTIC SEGMENTATION

Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks

CVPR 2017 tensorflow/models

Collecting well-annotated image datasets to train modern machine learning algorithms is prohibitively expensive for many tasks.

UNSUPERVISED DOMAIN ADAPTATION

Domain Separation Networks

NeurIPS 2016 tensorflow/models

However, by focusing only on creating a mapping or shared representation between the two domains, they ignore the individual characteristics of each domain.

UNSUPERVISED DOMAIN ADAPTATION

Visual Representations for Semantic Target Driven Navigation

15 May 2018tensorflow/models

We propose to using high level semantic and contextual features including segmentation and detection masks obtained by off-the-shelf state-of-the-art vision as observations and use deep network to learn the navigation policy.

DOMAIN ADAPTATION VISUAL NAVIGATION

Learning from Simulated and Unsupervised Images through Adversarial Training

CVPR 2017 tensorflow/models

With recent progress in graphics, it has become more tractable to train models on synthetic images, potentially avoiding the need for expensive annotations.

DOMAIN ADAPTATION GAZE ESTIMATION HAND POSE ESTIMATION IMAGE-TO-IMAGE TRANSLATION

Generalized End-to-End Loss for Speaker Verification

28 Oct 2017CorentinJ/Real-Time-Voice-Cloning

In this paper, we propose a new loss function called generalized end-to-end (GE2E) loss, which makes the training of speaker verification models more efficient than our previous tuple-based end-to-end (TE2E) loss function.

DOMAIN ADAPTATION SPEAKER VERIFICATION

Coupled Generative Adversarial Networks

NeurIPS 2016 eriklindernoren/Keras-GAN

We propose coupled generative adversarial network (CoGAN) for learning a joint distribution of multi-domain images.

DOMAIN ADAPTATION IMAGE-TO-IMAGE TRANSLATION

Unsupervised Image-to-Image Translation Networks

NeurIPS 2017 eriklindernoren/PyTorch-GAN

Unsupervised image-to-image translation aims at learning a joint distribution of images in different domains by using images from the marginal distributions in individual domains.

DOMAIN ADAPTATION MULTIMODAL UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION

Transfer Learning with Dynamic Distribution Adaptation

17 Sep 2019jindongwang/transferlearning

Since the source and the target domains are usually from different distributions, existing methods mainly focus on adapting the cross-domain marginal or conditional distributions.

DOMAIN ADAPTATION IMAGE CLASSIFICATION SENTIMENT ANALYSIS TRANSFER LEARNING

Easy Transfer Learning By Exploiting Intra-domain Structures

2 Apr 2019jindongwang/transferlearning

In this paper, we propose a practically Easy Transfer Learning (EasyTL) approach which requires no model selection and hyperparameter tuning, while achieving competitive performance.

DOMAIN ADAPTATION MODEL SELECTION TRANSFER LEARNING