Few-shot Classification via Adaptive Attention
arXiv:2008.02465
Abstract
Training a neural network model that can quickly adapt to a new task is highly desirable yet challenging for few-shot learning problems. Recent few-shot learning methods mostly concentrate on developing various meta-learning strategies from two aspects, namely optimizing an initial model or learning a distance metric. In this work, we propose a novel few-shot learning method via optimizing and fast adapting the query sample representation based on very few reference samples. To be specific, we devise a simple and efficient meta-reweighting strategy to adapt the sample representations and generate soft attention to refine the representation such that the relevant features from the query and support samples can be extracted for a better few-shot classification. Such an adaptive attention model is also able to explain what the classification model is looking for as the evidence for classification to some extent. As demonstrated experimentally, the proposed model achieves state-of-the-art classification results on various benchmark few-shot classification and fine-grained recognition datasets.
arXiv comment11 pages, 4 figures
References in corpus (15)
- Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
- Prototypical Networks for Few-shot Learning
- Wide Residual Networks
- Theoretical Models of Learning to Learn
- A Simple Neural Attentive Meta-Learner
- Meta-SGD: Learning to Learn Quickly for Few-Shot Learning
- Few-Shot Learning with Graph Neural Networks
- Residual Attention Network for Image Classification
- Delta-encoder: an effective sample synthesis method for few-shot object recognition
- Meta-Learning with Latent Embedding Optimization
- Piecewise classifier mappings: Learning fine-grained learners for novel categories with few examples
- How to train your MAML
- Generative Adversarial Residual Pairwise Networks for One Shot Learning
- Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learning
- Meta-Transfer Learning for Few-Shot Learning