4 papers
Forgetting, plasticity, and co-observation: a third facet of continual learning
Timm Hess, Abhishek Jha, Gido M. van de Ven +1
Efficient continual learning remains a fundamental challenge for deep neural networks. While catastrophic forgetting and loss of plasticity are widely considered the primary obstac…
Self-Supervised Learning with a Multi-Task Latent Space Objective
Pierre-François De Plaen, Abhishek Jha, Luc Van Gool +2
We propose a multi-task formulation of self-predictive Siamese SSL in which each spatial transformation defines a distinct latent-space alignment task, solved by a dedicated predic…
Unsupervised Parameter Efficient Source-free Post-pretraining
Abhishek Jha, Tinne Tuytelaars, Yuki M. Asano
Following the success in NLP, the best vision models are now in the billion parameter ranges. Adapting these large models to a target distribution has become computationally and ec…
Analysis of Spatial augmentation in Self-supervised models in the purview of training and test distributions
Abhishek Jha, Tinne Tuytelaars
In this paper, we present an empirical study of typical spatial augmentation techniques used in self-supervised representation learning methods (both contrastive and non-contrastiv…