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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence / EAAI-20

Active Generative Adversarial Network for Image Classification

February 1, 2023

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Authors

Quan Kong

Hitachi, Ltd.


Bin Tong

Hitachi, Ltd.


Martin Klinkigt

Hitachi, Ltd.


Yuki Watanabe

Hitachi America, Ltd.


Naoto Akira

Hitachi, Ltd.


Tomokazu Murakami

Hitachi, Ltd.


DOI:

10.1609/aaai.v33i01.33014090


Abstract:

Sufficient supervised information is crucial for any machine learning models to boost performance. However, labeling data is expensive and sometimes difficult to obtain. Active learning is an approach to acquire annotations for data from a human oracle by selecting informative samples with a high probability to enhance performance. In recent emerging studies, a generative adversarial network (GAN) has been integrated with active learning to generate good candidates to be presented to the oracle. In this paper, we propose a novel model that is able to obtain labels for data in a cheaper manner without the need to query an oracle. In the model, a novel reward for each sample is devised to measure the degree of uncertainty, which is obtained from a classifier trained with existing labeled data. This reward is used to guide a conditional GAN to generate informative samples with a higher probability for a certain label. With extensive evaluations, we have confirmed the effectiveness of the model, showing that the generated samples are capable of improving the classification performance in popular image classification tasks.

Topics: AAAI

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HOW TO CITE:

Quan Kong||Bin Tong||Martin Klinkigt||Yuki Watanabe||Naoto Akira||Tomokazu Murakami Active Generative Adversarial Network for Image Classification Proceedings of the AAAI Conference on Artificial Intelligence (2019) 4090-4097.

Quan Kong||Bin Tong||Martin Klinkigt||Yuki Watanabe||Naoto Akira||Tomokazu Murakami Active Generative Adversarial Network for Image Classification AAAI 2019, 4090-4097.

Quan Kong||Bin Tong||Martin Klinkigt||Yuki Watanabe||Naoto Akira||Tomokazu Murakami (2019). Active Generative Adversarial Network for Image Classification. Proceedings of the AAAI Conference on Artificial Intelligence, 4090-4097.

Quan Kong||Bin Tong||Martin Klinkigt||Yuki Watanabe||Naoto Akira||Tomokazu Murakami. Active Generative Adversarial Network for Image Classification. Proceedings of the AAAI Conference on Artificial Intelligence 2019 p.4090-4097.

Quan Kong||Bin Tong||Martin Klinkigt||Yuki Watanabe||Naoto Akira||Tomokazu Murakami. 2019. Active Generative Adversarial Network for Image Classification. "Proceedings of the AAAI Conference on Artificial Intelligence". 4090-4097.

Quan Kong||Bin Tong||Martin Klinkigt||Yuki Watanabe||Naoto Akira||Tomokazu Murakami. (2019) "Active Generative Adversarial Network for Image Classification", Proceedings of the AAAI Conference on Artificial Intelligence, p.4090-4097

Quan Kong||Bin Tong||Martin Klinkigt||Yuki Watanabe||Naoto Akira||Tomokazu Murakami, "Active Generative Adversarial Network for Image Classification", AAAI, p.4090-4097, 2019.

Quan Kong||Bin Tong||Martin Klinkigt||Yuki Watanabe||Naoto Akira||Tomokazu Murakami. "Active Generative Adversarial Network for Image Classification". Proceedings of the AAAI Conference on Artificial Intelligence, 2019, p.4090-4097.

Quan Kong||Bin Tong||Martin Klinkigt||Yuki Watanabe||Naoto Akira||Tomokazu Murakami. "Active Generative Adversarial Network for Image Classification". Proceedings of the AAAI Conference on Artificial Intelligence, (2019): 4090-4097.

Quan Kong||Bin Tong||Martin Klinkigt||Yuki Watanabe||Naoto Akira||Tomokazu Murakami. Active Generative Adversarial Network for Image Classification. AAAI[Internet]. 2019[cited 2023]; 4090-4097.


ISSN: 2374-3468


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