Search Results for author:
Found 6 papers, 3 papers with code
DyTox: Transformers for Continual Learning with DYnamic TOken eXpansion
Our strategy scales to a large number of tasks while having negligible memory and time overheads due to strict control of the parameters expansion.
Ranked #2 on
Incremental Learning
on ImageNet - 10 steps
Diverse Weight Averaging for Out-of-Distribution Generalization
Standard neural networks struggle to generalize under distribution shifts in computer vision.
Model Ratatouille: Recycling Diverse Models for Out-of-Distribution Generalization
In this paper, we thus propose model ratatouille, a new strategy to recycle the multiple fine-tunings of the same foundation model on diverse auxiliary tasks.
Ranked #14 on
Domain Generalization
on PACS
Leveraging Weakly Annotated Data for Fashion Image Retrieval and Label Prediction
In this paper, we present a method to learn a visual representation adapted for e-commerce products.
Towards efficient feature sharing in MIMO architectures
To solve this issue, we propose a novel unmixing step in MIMO architectures that allows subnetworks to properly share features.
WARM: On the Benefits of Weight Averaged Reward Models
We identify two primary challenges when designing RMs to mitigate reward hacking: distribution shifts during the RL process and inconsistencies in human preferences.
