Browse SoTA > Methodology > Domain Adaptation

Domain Adaptation

564 papers with code · Methodology

Domain adaptation is the task of adapting models across 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 OBJECT DETECTION PERSON RE-IDENTIFICATION RETINAL OCT DISEASE CLASSIFICATION SEMANTIC SEGMENTATION

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

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

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

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

Data Valuation using Reinforcement Learning

ICML 2020 google-research/google-research

To adaptively learn data values jointly with the target task predictor model, we propose a meta learning framework which we name Data Valuation using Reinforcement Learning (DVRL).

DOMAIN ADAPTATION META-LEARNING

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

Coupled Generative Adversarial Networks

NeurIPS 2016 eriklindernoren/PyTorch-GAN

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

DOMAIN ADAPTATION IMAGE-TO-IMAGE TRANSLATION