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GTC ON-DEMAND

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Abstract:

Generative adversarial networks (GANs) have been applied for multiple cases, such as generating images and image completion. One interesting feature of GANs is the exploration in latent space, where new elements can appear caused by the interpolation between two seed elements. With this in mind, we're interested in exploring latent space in terms of adjective-noun pairs (ANP) able to capture subjectivity in visual content such as "cloudy sky" vs. "pretty sky." Although it is challenging for humans to find a smooth transition between two ANPs (similar to color gradient or color progression), the presented GANs are capable of generating such a gradient in the adjective domain and find new ANPs that lie in this (subjective) transition. As result, GANs offer a more quantified interpretation for this subjective progression and an explainability of the underlying latent space.

Generative adversarial networks (GANs) have been applied for multiple cases, such as generating images and image completion. One interesting feature of GANs is the exploration in latent space, where new elements can appear caused by the interpolation between two seed elements. With this in mind, we're interested in exploring latent space in terms of adjective-noun pairs (ANP) able to capture subjectivity in visual content such as "cloudy sky" vs. "pretty sky." Although it is challenging for humans to find a smooth transition between two ANPs (similar to color gradient or color progression), the presented GANs are capable of generating such a gradient in the adjective domain and find new ANPs that lie in this (subjective) transition. As result, GANs offer a more quantified interpretation for this subjective progression and an explainability of the underlying latent space.

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Topics:
Artificial Intelligence and Deep Learning, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7608
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