Search Results for author:
Found 2 papers, 1 papers with code
Self-Rule to Multi-Adapt: Generalized Multi-source Feature Learning Using Unsupervised Domain Adaptation for Colorectal Cancer Tissue Detection
In this work, we propose Self-Rule to Multi-Adapt (SRMA), which takes advantage of self-supervised learning to perform domain adaptation, and removes the necessity of fully-labeled source datasets.
A Comprehensive Study of ImageNet Pre-Training for Historical Document Image Analysis
Automatic analysis of scanned historical documents comprises a wide range of image analysis tasks, which are often challenging for machine learning due to a lack of human-annotated learning samples.
Ranked #7 on
Image Classification
on Kuzushiji-MNIST
Cannot find the paper you are looking for? You can
Submit a new open access paper.
