Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DGL-GAN: Discriminator Guided Learning for GAN Compression   Generative Adversarial Networks (GANs) with high computation costs, e.g.,
BigGAN and StyleGAN2, have achieved remarkable results in synthesizing high
resolution and diverse images with high fidelity from random noises. Reducing
the computation cost of GANs while keeping generating photo-realistic images is
an urgent and challenging field for their broad applications on computational
resource-limited devices. In this work, we propose a novel yet simple {\bf
D}iscriminator {\bf G}uided {\bf L}earning approach for compressing vanilla
{\bf GAN}, dubbed {\bf DGL-GAN}. Motivated by the phenomenon that the teacher
discriminator may contain some meaningful information, we transfer the
knowledge merely from the teacher discriminator via the adversarial function.
We show DGL-GAN is valid since empirically, learning from the teacher
discriminator could facilitate the performance of student GANs, verified by
extensive experimental findings. Furthermore, we propose a two-stage training
strategy for training DGL-GAN, which can largely stabilize its training process
and achieve superior performance when we apply DGL-GAN to compress the two most
representative large-scale vanilla GANs, i.e., StyleGAN2 and BigGAN.
Experiments show that DGL-GAN achieves state-of-the-art (SOTA) results on both
StyleGAN2 (FID 2.92 on FFHQ with nearly $1/3$ parameters of StyleGAN2) and
BigGAN (IS 93.29 and FID 9.92 on ImageNet with nearly $1/4$ parameters of
BigGAN) and also outperforms several existing vanilla GAN compression
techniques. Moreover, DGL-GAN is also effective in boosting the performance of
original uncompressed GANs, original uncompressed StyleGAN2 boosted with
DGL-GAN achieves FID 2.65 on FFHQ, which achieves a new state-of-the-art
performance. Code and models are available at
\url{https://github.com/yuesongtian/DGL-GAN}.

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The categories of this paper are: dg-ga
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The categories of this paper are: mtrl-th
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The categories of this paper are: cmp-lg
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The categories of this paper are: patt-sol
0
The categories of this paper are: hep-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: math
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The categories of this paper are: plasm-ph
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The categories of this paper are: stat
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The categories of this paper are: ao-sci
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The categories of this paper are: atom-ph
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The categories of this paper are: eess
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The categories of this paper are: funct-an
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The categories of this paper are: econ
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The categories of this paper are: chao-dyn
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The categories of this paper are: bayes-an