Proposes cINN architecture for conditional image generation that by construction yields diverse sharp samples, demonstrated on MNIST digit generation and image colorization with latent space manipulation.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CV 2years
2019 2verdicts
UNVERDICTED 2representative citing papers
Introduces IFM loss regularization for CNNs to learn correlated discriminative features, tested on shiftedMNIST dataset.
citing papers explorer
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Guided Image Generation with Conditional Invertible Neural Networks
Proposes cINN architecture for conditional image generation that by construction yields diverse sharp samples, demonstrated on MNIST digit generation and image colorization with latent space manipulation.
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Learning to Find Correlated Features by Maximizing Information Flow in Convolutional Neural Networks
Introduces IFM loss regularization for CNNs to learn correlated discriminative features, tested on shiftedMNIST dataset.