4 citations · 4 across the 2 of their papers we have counts for
8 papers
Elastic Weight Consolidation Improves the Robustness of Self-Supervised Learning Methods under Transfer
Andrius Ovsianas, Jason Ramapuram, Dan Busbridge +2
Self-supervised representation learning (SSL) methods provide an effective label-free initial condition for fine-tuning downstream tasks. However, in numerous realistic scenarios,…
Evaluating the fairness of fine-tuning strategies in self-supervised learning
Jason Ramapuram, Dan Busbridge, Russ Webb
In this work we examine how fine-tuning impacts the fairness of contrastive Self-Supervised Learning (SSL) models. Our findings indicate that Batch Normalization (BN) statistics pl…
Self-Supervised MultiModal Versatile Networks
Jean-Baptiste Alayrac, Adrià Recasens, Rosalia Schneider +6
Videos are a rich source of multi-modal supervision. In this work, we learn representations using self-supervision by leveraging three modalities naturally present in videos: visua…
Improving Discrete Latent Representations With Differentiable Approximation Bridges
Jason Ramapuram, Russ Webb
Modern neural network training relies on piece-wise (sub-)differentiable functions in order to use backpropagation to update model parameters. In this work, we introduce a novel me…
Variational Saccading: Efficient Inference for Large Resolution Images
Jason Ramapuram, Maurits Diephuis, Frantzeska Lavda +2
Image classification with deep neural networks is typically restricted to images of small dimensionality such as 224 x 244 in Resnet models [24]. This limitation excludes the 4000…
Continual Classification Learning Using Generative Models
Frantzeska Lavda, Jason Ramapuram, Magda Gregorova +1
Continual learning is the ability to sequentially learn over time by accommodating knowledge while retaining previously learned experiences. Neural networks can learn multiple task…