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

A Forest from the Trees: Generation through Neighborhoods

February 1, 2023

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Authors

Yang Li

UNC Chapel Hill


Tianxiang Gao

UNC Chapel Hill


Junier Oliva

UNC Chapel Hill


DOI:

10.1609/aaai.v34i04.5909


Abstract:

In this work, we propose to learn a generative model using both learned features (through a latent space) and memories (through neighbors). Although human learning makes seamless use of both learned perceptual features and instance recall, current generative learning paradigms only make use of one of these two components. Take, for instance, flow models, which learn a latent space that follows a simple distribution. Conversely, kernel density techniques use instances to shift a simple distribution into an aggregate mixture model. Here we propose multiple methods to enhance the latent space of a flow model with neighborhood information. Not only does our proposed framework represent a more human-like approach by leveraging both learned features and memories, but it may also be viewed as a step forward in non-parametric methods. In addition, our proposed framework allows the user to easily control the properties of generated samples by targeting samples based on neighbors. The efficacy of our model is shown empirically with standard image datasets. We observe compelling results and a significant improvement over baselines. Combined further with a contrastive training mechanism, our proposed methods can effectively perform non-parametric novelty detection.

Topics: AAAI

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HOW TO CITE:

Yang Li||Tianxiang Gao||Junier Oliva A Forest from the Trees: Generation through Neighborhoods Proceedings of the AAAI Conference on Artificial Intelligence (2020) 4755-4762.

Yang Li||Tianxiang Gao||Junier Oliva A Forest from the Trees: Generation through Neighborhoods AAAI 2020, 4755-4762.

Yang Li||Tianxiang Gao||Junier Oliva (2020). A Forest from the Trees: Generation through Neighborhoods. Proceedings of the AAAI Conference on Artificial Intelligence, 4755-4762.

Yang Li||Tianxiang Gao||Junier Oliva. A Forest from the Trees: Generation through Neighborhoods. Proceedings of the AAAI Conference on Artificial Intelligence 2020 p.4755-4762.

Yang Li||Tianxiang Gao||Junier Oliva. 2020. A Forest from the Trees: Generation through Neighborhoods. "Proceedings of the AAAI Conference on Artificial Intelligence". 4755-4762.

Yang Li||Tianxiang Gao||Junier Oliva. (2020) "A Forest from the Trees: Generation through Neighborhoods", Proceedings of the AAAI Conference on Artificial Intelligence, p.4755-4762

Yang Li||Tianxiang Gao||Junier Oliva, "A Forest from the Trees: Generation through Neighborhoods", AAAI, p.4755-4762, 2020.

Yang Li||Tianxiang Gao||Junier Oliva. "A Forest from the Trees: Generation through Neighborhoods". Proceedings of the AAAI Conference on Artificial Intelligence, 2020, p.4755-4762.

Yang Li||Tianxiang Gao||Junier Oliva. "A Forest from the Trees: Generation through Neighborhoods". Proceedings of the AAAI Conference on Artificial Intelligence, (2020): 4755-4762.

Yang Li||Tianxiang Gao||Junier Oliva. A Forest from the Trees: Generation through Neighborhoods. AAAI[Internet]. 2020[cited 2023]; 4755-4762.


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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