Prototypical networksWe propose a Behavior Regularized Prototypical Network (BR-ProtoNet) for few-shot image classification in semi-supervised scenarios. To learn a generalizable metric, we exploit readily-available unlabeled data and construct complementary constraints to regularize the model's behavior....
Prototypical Networks for Few-shot Learning 论文简述 论文重点 想法 算法 算法细节 思考 论文简述 本文提出了原型网络,原型网络从数据中学习某种非线性的embedding,使得最终生成的度量空间中,每个类的点都聚集在单一的原型表示周围。对于询问点,只需要计算出在度量空间中距离最短的原型,并分类即可。 论文重点 想法 ...
论文笔记:Hybrid Attention-Based Prototypical Networks for Noisy Few-Shot Relation Classification,程序员大本营,技术文章内容聚合第一站。
Prototypical Networks for Few-shot Learningarxiv.org/abs/1703.05175 2.摘要: 该文提出了一种可以用于few-shot learning的原形网络(prototypical networks)。该网络能识别出在训练过程中从未见过的新的类别,并且对于每个类别只需要很少的样例数据。原形网络将每个类别中的样例数据映射到一个空间当中,并且提取他们...
Inspired by these works, this paper proposed a novel metric-based few-shot approach, named as Self-Supervised Learning Prototypical Networks (SSL-ProtoNet) for few-shot image classification. The SSL-ProtoNet is separated into 3 stages: pre-training, fine-tuning, and self-distillation. The ...
经过这两个阶段之后,所提出的SSL-ProtoNet能够成功地适应目标的few-shot任务。此外,所提出的SSL-ProtoNet也能够保持模型表示的多样性。生成的原型向量更接近目标查询样本,从而提高了所提出的SSL-ProtoNet的性能。在算法2中给出了微调阶段的训练过程 3.3. Self-distillation stage ...
论文信息:Snell J, Swersky K, Zemel R. Prototypical networks for few-shot learning[C]//Advances in Neural Information Processing Systems. 2017: 4077-4087.博文作者:Veagau编辑时间:2020年01月07日本文是2017年NIPS的会议论文,作者来自多伦多大学以及Twitter公司。在论文中作者提出了一种新的基于度量(Metric...
Prototypical Networks for Few-shot LearningJake SnellUniversity of Toronto ∗Vector InstituteKevin SwerskyTwitterRichard ZemelUniversity of TorontoVector InstituteCanadian Institute for Advanced ResearchAbstractWe propose Prototypical Networks for the problem of few-shot classif ication, wherea classif ier ...
Prototypical Networks for Few-shot Learning 摘要 我们提出了原型网络,用于解决少样本分类问题,在这种情况下,分类器必须对训练集中未见的新类进行归纳,而每个新类只有少量的例子。原型网络学习一个度量空间,在这个空间中,可以通过计算与每个类别的原型表示的距离来进行分类。与最近的少样本学习方法相比,它们反映了一种...
Prototypical network (PN) is a simple yet effective few shot learning strategy. It is a metric-based meta-learning technique where classification is performed by computing Euclidean distances to prototypical representations of each class. Conventional PN attributes equal importance to all samples and ...