Synthetic-to-Real Translation

32 papers with code • 3 benchmarks • 4 datasets

Synthetic-to-real translation is the task of domain adaptation from synthetic (or virtual) data to real data.

( Image credit: CYCADA )

Greatest papers with code

Virtual to Real Reinforcement Learning for Autonomous Driving

SullyChen/Autopilot-TensorFlow 13 Apr 2017

To our knowledge, this is the first successful case of driving policy trained by reinforcement learning that can adapt to real world driving data.

Autonomous Driving Domain Adaptation +3

Diverse Image-to-Image Translation via Disentangled Representations

HsinYingLee/DRIT ECCV 2018

Our model takes the encoded content features extracted from a given input and the attribute vectors sampled from the attribute space to produce diverse outputs at test time.

Domain Adaptation Multimodal Unsupervised Image-To-Image Translation +2

Domain Adaptation for Structured Output via Discriminative Patch Representations

wasidennis/AdaptSegNet ICCV 2019

Predicting structured outputs such as semantic segmentation relies on expensive per-pixel annotations to learn supervised models like convolutional neural networks.

Domain Adaptation Semantic Segmentation +1

Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation

layumi/Seg-Uncertainty 8 Mar 2020

This paper focuses on the unsupervised domain adaptation of transferring the knowledge from the source domain to the target domain in the context of semantic segmentation.

Synthetic-to-Real Translation Unsupervised Domain Adaptation +1

Unsupervised Scene Adaptation with Memory Regularization in vivo

layumi/Seg-Uncertainty 24 Dec 2019

We consider the unsupervised scene adaptation problem of learning from both labeled source data and unlabeled target data.

Domain Adaptation Semantic Segmentation +1