Search Results for author:
Found 6 papers, 3 papers with code
Benchmarking the Fairness of Image Upsampling Methods
Recent years have witnessed a rapid development of deep generative models for creating synthetic media, such as images and videos.
Identifiability Results for Multimodal Contrastive Learning
Our work generalizes previous identifiability results by redefining the generative process in terms of distinct mechanisms with modality-specific latent variables.
How Robust is Unsupervised Representation Learning to Distribution Shift?
As such, there is a lack of insight on the robustness of the representations learned from unsupervised methods, such as self-supervised learning (SSL) and auto-encoder based algorithms (AE), to distribution shift.
On the Limitations of Multimodal VAEs
Multimodal variational autoencoders (VAEs) have shown promise as efficient generative models for weakly-supervised data.
Generalized Multimodal ELBO
Multiple data types naturally co-occur when describing real-world phenomena and learning from them is a long-standing goal in machine learning research.
Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence
Learning from different data types is a long-standing goal in machine learning research, as multiple information sources co-occur when describing natural phenomena.
