2 papers
cs.LG2023
Augment to Interpret: Unsupervised and Inherently Interpretable Graph Embeddings
Gregory Scafarto, Madalina Ciortan, Simon Tihon +1
Unsupervised learning allows us to leverage unlabelled data, which has become abundantly available, and to create embeddings that are usable on a variety of downstream tasks. Howev…
cs.CV2021
A Framework using Contrastive Learning for Classification with Noisy Labels
Madalina Ciortan, Romain Dupuis, Thomas Peel
We propose a framework using contrastive learning as a pre-training task to perform image classification in the presence of noisy labels. Recent strategies such as pseudo-labeling,…