Mar 18, 2024 · We define this problem as continual forgetting and identify two key challenges. (i) For unwanted knowledge, efficient and effective deleting is ...

This is the official implementation of GS-LoRA (CVPR 2024). GS-LoRA is effective, parameter-efficient, data-efficient, and easy to implement continual ...

Mar 18, 2024 · This paper presents a new and practical problem called continual forgetting and proposes an efficient and effective method to solve it. For each ...

To address them, we propose. Group Sparse LoRA (GS-LoRA). Specifically, towards. (i), we use LoRA modules to fine-tune the FFN layers in. Transformer blocks for ...

Visualization of detection results. We provide visualiza- tion results on COCO validation set before and after for- getting. Fig.

Continual Forgetting for Pre-trained Vision Models ... (i) For unwanted knowledge, efficient and effective deleting is crucial. 3.

8 days ago · We investigate the characteristics of the Continual Pre-Training scenario, where a model is continually pre-trained on a stream of incoming data ...

Continual Forgetting in pre-trained vision models focuses on the removal of specific unwanted knowledge. The approach dubbed Group Sparse LoRA (GS-LoRA) ...

Early approaches start training with a model from scratch, continually adapt the model for future tasks, and prevent forgetting by replaying the data, designing ...

We formalize and investigate the characteristics of the continual pre-training scenario in both language and vision environments, where a model is continually ...