Domain Adaptation

794 papers with code • 32 benchmarks • 55 datasets

Domain adaptation is the task of adapting models across domains.

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

Greatest papers with code

Visual Representations for Semantic Target Driven Navigation

tensorflow/models 15 May 2018

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

tensorflow/models NeurIPS 2016

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

Deep Residual Learning for Image Recognition

tensorflow/models CVPR 2016

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.

Breast Tumour Classification Domain Generalization +8

Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks

tensorflow/models CVPR 2017

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

Unsupervised Domain Adaptation

Generalized End-to-End Loss for Speaker Verification

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

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

We Need to Talk About Random Splits

google-research/google-research EACL 2021

We argue that random splits, like standard splits, lead to overly optimistic performance estimates.

Domain Adaptation

Data Valuation using Reinforcement Learning

google-research/google-research ICML 2020

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

Language Models are Few-Shot Learners

openai/gpt-3 NeurIPS 2020

By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do.

Common Sense Reasoning Coreference Resolution +9

Unsupervised Image-to-Image Translation Networks

eriklindernoren/PyTorch-GAN NeurIPS 2017

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 +1

Coupled Generative Adversarial Networks

eriklindernoren/PyTorch-GAN NeurIPS 2016

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

Domain Adaptation Image-to-Image Translation