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Found 5 papers, 5 papers with code
Hard-aware Instance Adaptive Self-training for Unsupervised Cross-domain Semantic Segmentation
The divergence between labeled training data and unlabeled testing data is a significant challenge for recent deep learning models.
Instance Adaptive Self-Training for Unsupervised Domain Adaptation
In this paper, we propose an instance adaptive self-training framework for UDA on the task of semantic segmentation.
Ranked #13 on
Image-to-Image Translation
on SYNTHIA-to-Cityscapes
3rd Place Solution to "Google Landmark Retrieval 2020"
Image retrieval is a fundamental problem in computer vision.
Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet
The automatic and objective medical diagnostic model can be valuable to achieve early cancer detection, and thus reducing the mortality rate.
Ranked #1 on
Tumor Segmentation
on DigestPath
Cross-stained Segmentation from Renal Biopsy Images Using Multi-level Adversarial Learning
Experimental results on glomeruli segmentation from renal biopsy images indicate that our network is able to improve segmentation performance on target type of stained images and use unlabeled data to achieve similar accuracy to labeled data.
