Description
Chair: Roberto Covino
Deep Neural Networks (DNNs) have excelled in many fields, largely due to their proficiency in supervised learning tasks. However, the dependence on vast labeled data becomes a constraint when such data is scarce. Self-Supervised Learning (SSL), a promising approach, harnesses unlabeled data to derive meaningful representations. Yet, how SSL filters irrelevant information without explicit...
In the machine learning community, structured representations have demonstrated themselves to be hugely beneficial for efficient learning from limited data and generalization far beyond the training set. Examples of such structured representations include the spatially organized feature maps of convolutional neural networks, and the group structured activations of other equivariant models. To...
