Meta-learning with implicit gradients--nips19 论文思想 Few-shot case formula Implicit MAML Algorithm Practical Algorithm 论文思想 原始的MAML算法一个很大的挑战是外循环(元更新)需要通过对内循环(梯度自适应)过程进行求导,一般就要求存储和计算高阶导数。这篇论文的核心是利用隐微分方法,... ...
Few-shot Learning aims to learn classifiers for new classes with only a few training examples per class. Existing meta-learning or metric-learning based few-shot learning approaches are limited in handling diverse domains with various number of labels. The meta-learning approaches train a meta lea...
3. Few-Shot Learning with Meta Metric Learners 该文提到现存小样本学习要么基于元学习(Meta-Learning),要么基于度量学习(Metric-Learning),度量学习也叫相似度学习。因此作者就想到能不能这两种学习结合起来,产生一个元度量学习(Meta Metric Learning)。事实上作者还真的做到了,文中训练了一个度量学习器来学习特定...
Few-shot Learning with Meta Metric Learners. NIPS 2017 workshop on Meta-Learning, arXiv'1901.09890 Microsoft AI & Research, IBM Research AI, JD AI Research Sentence Classification Services / Omniglot / Amazon Reviews Existing meta-learning or metric-learning based few-shot learning approaches are ...
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, ...
Few-Shot Learning (FSL) is a Machine Learning framework that enables a pre-trained model to generalize over new categories of data (that the pre-trained model has not seen during training) using only a few labeled samples per class. It falls under the paradigm of meta-learning (meta-learnin...
Few Shot Learning（FSL）又称少样本学习，这是做AI研究经常遇到的一个问题。深度学习技术需要大量的数据...
Facial expression recognition (FER) is utilized in various fields that analyze facial expressions. FER is attracting increasing attention for its role in i
1)Application to other metric Learners: 在度量学习中,在多个抽象层级进行特征比较,对深度网络的学习噪声正则器的value。 2)Ablation study:Deep supervision,Module Weighting,Metrics,Multiple Non-linear 4)Relation module Analysis:A key contribution in DCN is to perform metric learning at multiple abstraction...
for the scenario where a set amount of updates will be made, while also learning a general initialization of the learner network that allows for quick convergence of training. We demonstrate that this meta-learning model is competitive with deep metric-learning techniques for few-shot learning. ...