Search Results for author:
Found 2 papers, 0 papers with code
Harmony: A Generic Unsupervised Approach for Disentangling Semantic Content From Parameterized Transformations
Harmony leverages a simple cross-contrastive learning framework with multiple explicitly parameterized latent representations to disentangle content from transformations.
End-to-End Robust Joint Unsupervised Image Alignment and Clustering
To our knowledge, Jim-Net is the first end-to-end model that can simultaneously align and cluster images, which significantly improves the performance as compared to performing each task alone.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
