源代码链接:https://github.com/yinboc/few-shot-meta-baseline 背景知识: meta-learning(元学习) 本质是一种“learning to learn”的学习过程,不同于常用的深度学习模型(依据数据集去学习如何预测或者分类),meta-learning是学习“如何更快学习一个模型”的过程 MAML算法:Model-Agnostic Meta-Learning for Fast Ada...
code:https://github.com/cyvius96/few-shot-meta-baseline。 关于元学习和few-shot的基本内容有个很好的解释:Model-Agnostic Meta-Learning (MAML)模型介绍及算法详解(转载。 baseline包括两部分:classifier-baseline和Meta-Baseline。 classifier-baseline:在base类上预训练一个分类器,然后移去最后一个分类器。把nove...
Content provided by Yinbo Chen, the first author of the paper A New Meta-Baseline for Few-Shot Learning. Meta-learning has become a popular framework for few-shot learning in recent years. While more and more novel meta-learning models are being proposed, our research has uncovered simple...
A New Meta-Baseline for Few-Shot Learning. Contribute to chenso121/few-shot-meta-baseline development by creating an account on GitHub.
In this paper, a pairwise-based meta learning(PML) method is proposed for few-shot image classification. Transitive transfer learning is used to fine tune the pre-trained Resnet50 model to get a feature encoder that is more suitable for few shot task. Th
Meta-learning, or learning to learn, has emerged as one of the prominent approaches for few-shot learning. It is proposed to train a meta-learner which can quickly generalize to new tasks with few examples [33,45,165,178]. A meta-learning procedure also involves learning at two levels, ...
Flamingo: a Visual Language Model for Few-Shot Learning NeurIPS 2022-04-29 Github Demo Multimodal Few-Shot Learning with Frozen Language Models NeurIPS 2021-06-25 - - Multimodal Chain-of-Thought TitleVenueDateCodeDemo EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought arXiv...
for general few-shot training and evaluation. We then provide a taxonomy and a gentle review of recent few-shot meta-learning methods, to help researchers quickly grasp the state-of-the-art methods in this field. Finally we summa- rize some vital challenges to conclude this review with new ...
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This approach has shown excellent performance in few-shot learning tasks, especially in one-shot and few-shot learning scenarios, effectively improving model accuracy and the capacity for generalization. Zhou et al. [17] proposed a new architecture based on Prototypical Networks, LDP-Net, employing...