Aug 18, 2023 · Specifically, we tune the scale and bias parameters of LayerNorm for each continual learning task, selecting them at inference time based on the ...
Official Repository of "On the Effectiveness of LayerNorm Tuning for Continual Learning in Vision Transformers" (Visual Continual Learning Workshop ICCV 2023).
Our approach, C-LN, improves the accuracy in ImageNet-R over a 10-task benchmark while training fewer parameters. Recent works [43, 45, 46] demonstrated how ...
We tune the scale and bias parameters of LayerNorm for each continual learning task, selecting them at inference time based on the similarity between task- ...
Sep 7, 2024 · Specifically, we tune the scale and bias parameters of LayerNorm for each continual learning task, selecting them at inference time based on the ...
The experiments show LayerNorm fine-tuning's superior adaptability and scalability, particularly in the context of large-scale Med-VLMs. We hope this work will ...
On the Effectiveness of LayerNorm Tuning for Continual Learning in Vision Transformers · Xavier ALAMEDA-PINEDA 2023/10/03 2024/03/11 Research, Software, Vision.
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Apr 25, 2024 · The experiments show LayerNorm fine-tuning's superior adaptability and scalability, particularly in the context of large-scale Med-VLMs. We hope ...
Nov 6, 2024 · This paper aims to learn each task by tuning the prompts in the direction orthogonal to the subspace spanned by previous tasks' features.
In this paper, we pro- pose a novel parameter-efficient tuning approach on CLIP to boost both the anti-forgetting and zero-shot abilities in continual learning.