Exploring Internal Representations of Deep Neural Networks

Despraz, Jérémie; Gomez, Stéphane; Satizábal, Héctor F.; Peña-Reyes, Carlos Andrés

doi:10.1007/978-3-030-16469-0_7

Cited by 1 publication

(1 citation statement)

References 14 publications

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“…Further, Despraz and Gomez strongly activate individual neurons or groups of neurons of a convolutional neural network (CNN). They let a DGL model illustrate the effects on the CNN's internal representations by producing respective output images to foster model transparency and understanding [16]. Proceeding to audio data generation, DGL models frequently generate music.…”

Section: Creatingmentioning

confidence: 99%

Innovating with Artificial Intelligence: Capturing the Constructive Functional Capabilities of Deep Generative Learning

Hofmann¹,

Rückel²,

Urbach³

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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As an emerging species of artificial intelligence, deep generative learning models can generate an unprecedented variety of new outputs. Examples include the creation of music, text-to-image translation, or the imputation of missing data. Similar to other AI models that already evoke significant changes in society and economy, there is a need for structuring the constructive functional capabilities of DGL. To derive and discuss them, we conducted an extensive and structured literature review. Our results reveal a substantial scope of six constructive functional capabilities demonstrating that DGL is not exclusively used to generate unseen outputs. Our paper further guides companies in capturing and evaluating DGL's potential for innovation. Besides, our paper fosters an understanding of DGL and provides a conceptual basis for further research.

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Section: Creatingmentioning

confidence: 99%