update date

content/publication/LAE/index.md

@@ -17,7 +17,7 @@ publication_short: ICCV 2023
 abstract: "The 'pre-training → downstream adaptation' presents both new opportunities and challenges for Continual Learning (CL). Although the recent state-of-the-art in CL is achieved through Parameter-Efficient-Tuning (PET) adaptation paradigm, only prompt has been explored, limiting its application to Transformers only. In this paper, we position prompting as one instantiation of PET, and propose a unified CL framework with general PET, dubbed as Learning-Accumulation-Ensemble (LAE). PET, e.g., using Adapter, LoRA, or Prefix, can adapt a pre-trained model to downstream tasks with fewer parameters and resources. Given a PET method, our LAE framework incorporates it for CL with three novel designs. 1) Learning: the pre-trained model adapts to the new task by tuning an online PET module, along with our adaptation speed calibration to align different PET modules, 2) Accumulation: the task-specific knowledge learned by the online PET module is accumulated into an offline PET module through momentum update, 3) Ensemble: During inference, we respectively construct two experts with online/offline PET modules (which are favored by the novel/historical tasks) for prediction ensemble. We show that LAE is compatible with a battery of PET methods and gains strong CL capability. For example, LAE with Adaptor PET surpasses the prior state-of-the-art by 1.3% and 3.6% in last-incremental accuracy on CIFAR100 and ImageNet-R datasets, respectively."
 
 draft: false
-featured: true
+featured: false
 tags:
   - Deep learning
   - Continual learning
@@ -36,9 +36,9 @@ url_video: ""
 author_notes: []
 doi: ""
 projects: []
-date: 2021-01-16T00:00:02.020Z
+date: 2023-10-16T00:00:02.020Z
 url_slides: ""
-publishDate: 2021
+publishDate: 2023
 url_poster: ""
 url_code: 
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