| Paper ID | MLSP-1.1 |
| Paper Title |
META-LEARNING WITH ATTENTION FOR IMPROVED FEW-SHOT LEARNING |
| Authors |
Zejiang Hou, Princeton University, United States; Anwar Walid, Nokia Bell Labs, United States; Sun-Yuan Kung, Princeton University, United States |
| Session | MLSP-1: Deep Learning Training Methods 1 |
| Location | Gather.Town |
| Session Time: | Tuesday, 08 June, 13:00 - 13:45 |
| Presentation Time: | Tuesday, 08 June, 13:00 - 13:45 |
| Presentation |
Poster
|
| Topic |
Machine Learning for Signal Processing: [MLR-DEEP] Deep learning techniques |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
We consider few-shot learning (FSL), where a model learns from very few labeled examples such that it can generalize to unseen examples. Model-agnostic meta-learning (MAML) has been proposed to solve FSL. However, the low performance of MAML suggests its difficulty in tackle diverse tasks, due to the restriction of sharing a single model initialization for fast adaptation. In this paper, we propose meta-learning with attention mechanisms. Our method meta-learns attention modules to instantiate task-specific model initialization for fast adaptation, which can obtain high-quality solution to a new task using few gradient descent steps. To further improve generalization during inference, we propose to incorporate an entropy regularizer into the adaptation objective to penalize the Shannon entropy of prediction probability. Extensive experiments under various FSL scenarios show that our method achieves state-of-the-art performance on the mini-ImageNet and tiered-ImageNet. |
