xiexinran / few_shot_learning

classical model code implementation of few-shot/one-shot lenaring, including siamese network, prototypical network, relation network, induction network

few-shot learning

Each folder contains an implementation of each model.

• data_helper. data processing
• model. model construction
• trainer. train model
• metrics. performance metrics
• config.json Configuration files for model parameters and training parameters

induction_network

• paper: Few-Shot Text Classification with Induction Network

relation_network

• paper: Learning to Compare: Relation Network for Few-Shot Learning

prototypical_network

• paper: Prototypical Networks for Few-shot Learning

siamese_network

• paper: Siamese Neural Networks for One-shot Image Recognition

ARSC data set

• the data from Amazon Review Data Set, arranged by Alibaba Group
• citation: Image-based recommendations on styles and substitutes J. McAuley, C. Targett, J. Shi, A. van den Hengel SIGIR, 2015
• citation: Mo Yu, Xiaoxiao Guo, Jinfeng Yi, Shiyu Chang, Saloni Potdar, Yu Cheng, Gerald Tesauro, Haoyu Wang, and Bowen Zhou. 2018. Diverse few-shot text classification with multiple metrics

word vector

• using glove word vector, you need download 300 dim glove word vector and place it in word_embedded dir.

note

• You can only use 2-way, and if you need to use other way, you can modify the data_helper.py file.
• Shot should not be more than 10, because there are few comments under some categories.
• The number of categories in prediction and training can not be equal.