Search Results for author:
Found 6 papers, 4 papers with code
Revisiting the Parameter Efficiency of Adapters from the Perspective of Precision Redundancy
Current state-of-the-art results in computer vision depend in part on fine-tuning large pre-trained vision models.
Detachedly Learn a Classifier for Class-Incremental Learning
In continual learning, model needs to continually learn a feature extractor and classifier on a sequence of tasks.
FacT: Factor-Tuning for Lightweight Adaptation on Vision Transformer
Recent work has explored the potential to adapt a pre-trained vision transformer (ViT) by updating only a few parameters so as to improve storage efficiency, called parameter-efficient transfer learning (PETL).
Convolutional Bypasses Are Better Vision Transformer Adapters
The pretrain-then-finetune paradigm has been widely adopted in computer vision.
Bypassing Logits Bias in Online Class-Incremental Learning with a Generative Framework
In our framework, a generative classifier which utilizes replay memory is used for inference, and the training objective is a pair-based metric learning loss which is proven theoretically to optimize the feature space in a generative way.
Alleviating Representational Shift for Continual Fine-tuning
We study a practical setting of continual learning: fine-tuning on a pre-trained model continually.
