2 citations · 4 across the 4 of their papers we have counts for
4 papers
On the Effectiveness of LayerNorm Tuning for Continual Learning in Vision Transformers
Thomas De Min, Massimiliano Mancini, Karteek Alahari +2
State-of-the-art rehearsal-free continual learning methods exploit the peculiarities of Vision Transformers to learn task-specific prompts, drastically reducing catastrophic forget…
Multi-Domain Learning with Modulation Adapters
Ekaterina Iakovleva, Karteek Alahari, Jakob Verbeek
Deep convolutional networks are ubiquitous in computer vision, due to their excellent performance across different tasks for various domains. Models are, however, often trained in…
Think Before You Act: Unified Policy for Interleaving Language Reasoning with Actions
Lina Mezghani, Piotr Bojanowski, Karteek Alahari +1
The success of transformer models trained with a language modeling objective brings a promising opportunity to the reinforcement learning framework. Decision Transformer is a step…
Self-Supervised Models are Continual Learners
Enrico Fini, Victor G. Turrisi da Costa, Xavier Alameda-Pineda +3
Self-supervised models have been shown to produce comparable or better visual representations than their supervised counterparts when trained offline on unlabeled data at scale. Ho…