DeepAI AI Chat
Log In Sign Up

Unsupervised Scene Adaptation with Memory Regularization in vivo

by   Zhedong Zheng, et al.
University of Technology Sydney

We consider the unsupervised scene adaptation problem of learning from both labeled source data and unlabeled target data. Existing methods focus on minoring the inter-domain gap between the source and target domains. However, the intra-domain knowledge and inherent uncertainty learned by the network are under-explored. In this paper, we propose an orthogonal method, called memory regularization in vivo to exploit the intra-domain knowledge and regularize the model training. Specifically, we refer to the segmentation model itself as the memory module, and minor the discrepancy of the two classifiers, i.e., the primary classifier and the auxiliary classifier, to reduce the prediction inconsistency. Without extra parameters, the proposed method is complementary to the most existing domain adaptation methods and could generally improve the performance of existing methods. Albeit simple, we verify the effectiveness of memory regularization on two semantic segmentation datasets: GTA5 -> Cityscapes and SYNTHIA -> Cityscapes, yielding +11.1 baseline model, respectively.


page 1

page 3


PiPa: Pixel- and Patch-wise Self-supervised Learning for Domain Adaptative Semantic Segmentation

Unsupervised Domain Adaptation (UDA) aims to enhance the generalization ...

Adaptation Across Extreme Variations using Unlabeled Domain Bridges

We tackle an unsupervised domain adaptation problem for which the domain...

Unsupervised Domain Adaptive Fundus Image Segmentation with Category-level Regularization

Existing unsupervised domain adaptation methods based on adversarial lea...

Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision

Convolutional neural network-based approaches have achieved remarkable p...

Feature Alignment by Uncertainty and Self-Training for Source-Free Unsupervised Domain Adaptation

Most unsupervised domain adaptation (UDA) methods assume that labeled so...

Unsupervised Adaptive Semantic Segmentation with Local Lipschitz Constraint

Recent advances in unsupervised domain adaptation have seen considerable...

Rethink Maximum Mean Discrepancy for Domain Adaptation

Existing domain adaptation methods aim to reduce the distributional diff...

Code Repositories


Uncertainty Aware Curriculum Domain Adaptation. Code for The UIoU Dark Zurich Challenge @ Vision for All Seasons Workshop, CVPR 2020

view repo