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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 32

Efficient K-Shot Learning With Regularized Deep Networks

March 15, 2023

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Published Date: 2018-02-08

Registration: ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)

Copyright: Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.

Authors

Donghyun Yoo

Carnegie Mellon University


Haoqi Fan

Facebook


Vishnu Boddeti

Michigan State University


Kris Kitani

Carnegie Mellon University, Robotics Institute


DOI:

10.1609/aaai.v32i1.11774


Abstract:

Feature representations from pre-trained deep neural networks have been known to exhibit excellent generalization and utility across a variety of related tasks. Fine-tuning is by far the simplest and most widely used approach that seeks to exploit and adapt these feature representations to novel tasks with limited data. Despite the effectiveness of fine-tuning, it is often sub-optimal and requires very careful optimization to prevent severe over-fitting to small datasets. The problem of sub-optimality and overfitting, is due in part to the large number of parameters used in a typical deep convolutional neural network. To address these problems, we propose a simple yet effective regularization method for fine-tuning pre-trained deep networks for the task of k-shot learning. To prevent overfitting, our key strategy is to cluster the model parameters while ensuring intra-cluster similarity and inter-cluster diversity of the parameters, effectively regularizing the dimensionality of the parameter search space. In particular, we identify groups of neurons within each layer of a deep network that shares similar activation patterns. When the network is to be fine-tuned for a classification task using only k examples, we propagate a single gradient to all of the neuron parameters that belong to the same group. The grouping of neurons is non-trivial as neuron activations depend on the distribution of the input data. To efficiently search for optimal groupings conditioned on the input data, we propose a reinforcement learning search strategy using recurrent networks to learn the optimal group assignments for each network layer. Experimental results show that our method can be easily applied to several popular convolutional neural networks and improve upon other state-of-the-art fine-tuning based k-shot learning strategies by more than 10%.

Topics: AAAI

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HOW TO CITE:

Donghyun Yoo||Haoqi Fan||Vishnu Boddeti||Kris Kitani Efficient K-Shot Learning With Regularized Deep Networks Proceedings of the AAAI Conference on Artificial Intelligence, 32 (2018) .

Donghyun Yoo||Haoqi Fan||Vishnu Boddeti||Kris Kitani Efficient K-Shot Learning With Regularized Deep Networks AAAI 2018, .

Donghyun Yoo||Haoqi Fan||Vishnu Boddeti||Kris Kitani (2018). Efficient K-Shot Learning With Regularized Deep Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 32, .

Donghyun Yoo||Haoqi Fan||Vishnu Boddeti||Kris Kitani. Efficient K-Shot Learning With Regularized Deep Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 32 2018 p..

Donghyun Yoo||Haoqi Fan||Vishnu Boddeti||Kris Kitani. 2018. Efficient K-Shot Learning With Regularized Deep Networks. "Proceedings of the AAAI Conference on Artificial Intelligence, 32". .

Donghyun Yoo||Haoqi Fan||Vishnu Boddeti||Kris Kitani. (2018) "Efficient K-Shot Learning With Regularized Deep Networks", Proceedings of the AAAI Conference on Artificial Intelligence, 32, p.

Donghyun Yoo||Haoqi Fan||Vishnu Boddeti||Kris Kitani, "Efficient K-Shot Learning With Regularized Deep Networks", AAAI, p., 2018.

Donghyun Yoo||Haoqi Fan||Vishnu Boddeti||Kris Kitani. "Efficient K-Shot Learning With Regularized Deep Networks". Proceedings of the AAAI Conference on Artificial Intelligence, 32, 2018, p..

Donghyun Yoo||Haoqi Fan||Vishnu Boddeti||Kris Kitani. "Efficient K-Shot Learning With Regularized Deep Networks". Proceedings of the AAAI Conference on Artificial Intelligence, 32, (2018): .

Donghyun Yoo||Haoqi Fan||Vishnu Boddeti||Kris Kitani. Efficient K-Shot Learning With Regularized Deep Networks. AAAI[Internet]. 2018[cited 2023]; .


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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