3 papers
cs.CV2024
Benchmarking Vision-Language Contrastive Methods for Medical Representation Learning
Shuvendu Roy, Yasaman Parhizkar, Franklin Ogidi +5
We perform a comprehensive benchmarking of contrastive frameworks for learning multimodal representations in the medical domain. Through this study, we aim to answer the following…
cs.CV2023
Random Field Augmentations for Self-Supervised Representation Learning
Philip Andrew Mansfield, Arash Afkanpour, Warren Richard Morningstar +1
Self-supervised representation learning is heavily dependent on data augmentations to specify the invariances encoded in representations. Previous work has shown that applying dive…
cs.LG2023
Federated Variational Inference: Towards Improved Personalization and Generalization
Elahe Vedadi, Joshua V. Dillon, Philip Andrew Mansfield +3
Conventional federated learning algorithms train a single global model by leveraging all participating clients' data. However, due to heterogeneity in client generative distributio…