Learning Functors using Gradient Descent
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3,940 citations
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"Learning Functors using Gradient De..." refers background or methods in this paper
...We show that for specific choices of Free(G)/∼ and the dataset we recover GAN [6] and CycleGAN [13]....
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...Motivated by the sucess of Generative Adversarial Networks (GANs) [6] in image generation, some existing unsupervised learning methods [1, 13] use adversarial losses to learn the true data distribution of given domains of natural images and cycle-consistency losses to learn coherent mappings between those domains....
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...Based on this construction, Figure 2 shows the interconnection pattern for generators of two popular neural network architectures: GAN [6] and CycleGAN [13]....
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6,273 citations
"Learning Functors using Gradient De..." refers background in this paper
...CelebFaces Attributes Dataset (CelebA) [10] is a large-scale face attributes dataset with more than 200000 celebrity images, each with 40 attribute annotations....
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4,133 citations
3,940 citations
"Learning Functors using Gradient De..." refers background or methods in this paper
...As eloquently described in the introduction of [15], often we can reason about stylistic differences between paintings of different painters, even though never having seen paired data, i....
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...Based on this construction, Figure 2 shows the interconnection pattern for generators of two popular neural network architectures: GAN [6] and CycleGAN [15]....
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...We generalize the training procedure described in [15] in a natural way, free of ad-hoc choices....
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...In this paper we build a category-theoretic formalism around a neural network system called CycleGAN [15]....
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...As such, learning inter-domain mappings has received increasing attention in recent years, especially in the context of unpaired data and image-to-image translation [15, 1]....
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