Compressing image-to-image models
AssigneeSnap Inc.
InventorsJian Ren, Oliver Woodford, Sergey Tulyakov, Jiazhuo Wang, Qing Jin
Life of this patent
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Abstract
Systems and methods herein describe an image compression system. The image compression system generates a first generative adversarial network (GAN), identifies a threshold, based on the threshold, generates a second GAN by pruning channels of the first GAN, trains the second GAN using similarity-based knowledge distillation from the first GAN, and stores the trained second GAN.
Claims20
A method comprising:
- generating a first generative adversarial network (GAN) comprising a first type of convolutional layer, a second type of convolutional layer and a plurality of kernel sizes;
- identifying a threshold, the threshold representing a measure of computational costs of the GAN;
- based on the threshold, generating a second GAN by pruning channels of the first GAN;
- training the second GAN using similarity-based knowledge distillation from the first GAN, the training comprising:
- computing a global similarity metric between features of the first GAN and features of the second GAN on channels of the first GAN and channels of the second GAN, the global similarity metric characterizing batch-wise and spatial-wise similarity;
- and
- storing the trained second GAN.
A system comprising:
- a processor;
- and
- a memory storing instructions that, when executed by the processor, configure the system to perform operations comprising:
- generating a first generative adversarial network (GAN) comprising a first type of convolutional layer, a second type of convolutional layer and a plurality of kernel sizes;
- identifying a threshold, the threshold representing a measure of computational costs of the GAN;
- based on the threshold, generating a second GAN by pruning channels of the first GAN;
- training the second GAN using similarity-based knowledge distillation from the first GAN, the training comprising:
- computing a global similarity metric between features of the first GAN and features of the second GAN on channels of the first GAN and channels of the second GAN, the global similarity metric characterizing batch-wise and spatial-wise similarity;
- and
- storing the trained second GAN.
A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to perform operations comprising:
- generating a first generative adversarial network (GAN) comprising a first type of convolutional layer, a second type of convolutional layer and a plurality of kernel sizes;
- identifying a threshold, the threshold representing a measure of computational costs of the GAN;
- based on the threshold, generating a second GAN by pruning channels of the first GAN;
- training the second GAN using similarity-based knowledge distillation from the first GAN, the training comprising:
- computing a global similarity metric between features of the first GAN and features of the second GAN on channels of the first GAN and channels of the second GAN, the global similarity metric characterizing batch-wise and spatial-wise similarity;
- and
- storing the trained second GAN.
Dependencies are inferred from the claim text.