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Oracle Guided Image Synthesis with Relative Queries

arXiv 2204.14189 ↗First posted 28 Apr 2022cs.CV cs.AIcited by 0

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Abstract (arXiv)

Isolating and controlling specific features in the outputs of generative models in a user-friendly way is a difficult and open-ended problem. We develop techniques that allow an oracle user to generate an image they are envisioning in their head by answering a sequence of relative queries of the form textit"do you prefer image a or image b?" Our framework consists of a Conditional VAE that uses the collected relative queries to partition the latent space into preference-relevant features and non-preference-relevant features. We then use the user's responses to relative queries to determine the preference-relevant features that correspond to their envisioned output image. Additionally, we develop techniques for modeling the uncertainty in images' predicted preference-relevant features, allowing our framework to generalize to scenarios in which the relative query training set contains noise.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 37 references that are in the map. First posted on arXiv 28 Apr 2022.

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14 of 37 references.

References · 14

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  11. 2018Disentangling Factors of Variation with Cycle-Consistent Variational Auto-Encoderscited by 49
  12. 2020Bayesian Triplet Loss: Uncertainty Quantification in Image Retrievalcited by 31
2 more
  1. 2015Bayesian representation learning with oracle constraintscited by 16
  2. 2019Active embedding search via noisy paired comparisonscited by 12

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