3 citations · 7 across the 13 of their papers we have counts for
6 papers · 1 filter
Context-Aware Multimodal Pretraining
Karsten Roth, Zeynep Akata, Dima Damen +2
Large-scale multimodal representation learning successfully optimizes for zero-shot transfer at test time. Yet the standard pretraining paradigm (contrastive learning on large amou…
Disentangled Representation Learning with the Gromov-Monge Gap
Théo Uscidda, Luca Eyring, Karsten Roth +3
Learning disentangled representations from unlabelled data is a fundamental challenge in machine learning. Solving it may unlock other problems, such as generalization, interpretab…
Reflecting on the State of Rehearsal-free Continual Learning with Pretrained Models
Lukas Thede, Karsten Roth, Olivier J. Hénaff +2
With the advent and recent ubiquity of foundation models, continual learning (CL) has recently shifted from continual training from scratch to the continual adaptation of pretraine…
ReNO: Enhancing One-step Text-to-Image Models through Reward-based Noise Optimization
Luca Eyring, Shyamgopal Karthik, Karsten Roth +2
Text-to-Image (T2I) models have made significant advancements in recent years, but they still struggle to accurately capture intricate details specified in complex compositional pr…
ETHER: Efficient Finetuning of Large-Scale Models with Hyperplane Reflections
Massimo Bini, Karsten Roth, Zeynep Akata +1
Parameter-efficient finetuning (PEFT) has become ubiquitous to adapt foundation models to downstream task requirements while retaining their generalization ability. However, the am…
Improving Intervention Efficacy via Concept Realignment in Concept Bottleneck Models
Nishad Singhi, Jae Myung Kim, Karsten Roth +1
Concept Bottleneck Models (CBMs) ground image classification on human-understandable concepts to allow for interpretable model decisions. Crucially, the CBM design inherently allow…