Journal of Computer and Knowledge Engineering, Year (2022-8)

Title : ( Meta-Learning for Medium-shot Sparse Learning via Deep Kernels )

Authors: Zohreh Adabi Firuzjaee , Kamaledin Ghiasi Shirazi ,

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Abstract

Few-shot learning assumes that we have a very small dataset for each task and trains a model on the set of tasks. For real-world problems, however, the amount of available data is substantially much more; we call this a medium-shot setting, where the dataset often has several hundreds of data. Despite their high accuracy, deep neural networks have a drawback as they are black-box. Learning interpretable models has become more important over time. This study aims to obtain sample-based interpretability using the attention mechanism. The main idea is reducing the task training data into a small number of support vectors using sparse kernel methods, and the model then predicts the test data of the task based on these support vectors. We propose a sparse medium-shot learning algorithm based on a metric-based Bayesian meta-learning algorithm whose output is probabilistic. Sparsity, along with uncertainty, effectively plays a key role in interpreting the model\\\\\\\'s behavior. In our experiments, we show that the proposed method provides significant interpretability by selecting a small number of support vectors and, at the same time, has a competitive accuracy compared to other less interpretable methods.

Keywords

, Bayesian Meta, Learning Medium, shot Learning Sample, based Interpretability Sparse Kernel Attention
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@article{paperid:1092451,
author = {Adabi Firuzjaee, Zohreh and Ghiasi Shirazi, Kamaledin},
title = {Meta-Learning for Medium-shot Sparse Learning via Deep Kernels},
journal = {Journal of Computer and Knowledge Engineering},
year = {2022},
month = {August},
issn = {2538-5453},
keywords = {Bayesian Meta-Learning Medium-shot Learning Sample-based Interpretability Sparse Kernel Attention},
}

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%0 Journal Article
%T Meta-Learning for Medium-shot Sparse Learning via Deep Kernels
%A Adabi Firuzjaee, Zohreh
%A Ghiasi Shirazi, Kamaledin
%J Journal of Computer and Knowledge Engineering
%@ 2538-5453
%D 2022

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