9 citations · 19 across the 19 of their papers we have counts for
19 papers
Adapt & Align: Continual Learning with Generative Models Latent Space Alignment
Kamil Deja, Bartosz Cywiński, Jan Rybarczyk +1
In this work, we introduce Adapt & Align, a method for continual learning of neural networks by aligning latent representations in generative models. Neural Networks suffer from ab…
CLIP-DINOiser: Teaching CLIP a few DINO tricks for open-vocabulary semantic segmentation
Monika Wysoczańska, Oriane Siméoni, Michaël Ramamonjisoa +3
The popular CLIP model displays impressive zero-shot capabilities thanks to its seamless interaction with arbitrary text prompts. However, its lack of spatial awareness makes it un…
On consequences of finetuning on data with highly discriminative features
Wojciech Masarczyk, Tomasz Trzciński, Mateusz Ostaszewski
In the era of transfer learning, training neural networks from scratch is becoming obsolete. Transfer learning leverages prior knowledge for new tasks, conserving computational res…
Bucks for Buckets (B4B): Active Defenses Against Stealing Encoders
Jan Dubiński, Stanisław Pawlak, Franziska Boenisch +2
Machine Learning as a Service (MLaaS) APIs provide ready-to-use and high-utility encoders that generate vector representations for given inputs. Since these encoders are very costl…
Revisiting Supervision for Continual Representation Learning
Daniel Marczak, Sebastian Cygert, Tomasz Trzciński +1
In the field of continual learning, models are designed to learn tasks one after the other. While most research has centered on supervised continual learning, there is a growing in…
Bayesian Flow Networks in Continual Learning
Mateusz Pyla, Kamil Deja, Bartłomiej Twardowski +1
Bayesian Flow Networks (BFNs) has been recently proposed as one of the most promising direction to universal generative modelling, having ability to learn any of the data type. The…