DGL-GAN: Discriminator Guided Learning for GAN Compression

• URL: http://arxiv.org/abs/2112.06502v2
• Date: Sun, 24 Mar 2024 15:12:20 GMT
• Title: DGL-GAN: Discriminator Guided Learning for GAN Compression
• Authors: Yuesong Tian, Li Shen, Xiang Tian, Dacheng Tao, Zhifeng Li, Wei Liu, Yaowu Chen,
• Abstract summary: Generative Adversarial Networks (GANs) with high computation costs have achieved remarkable results in synthesizing high-resolution images from random noise. We propose a novel yet simple bf Discriminator bf Guided bf Learning approach for compressing vanilla bf GAN, dubbed bf DGL-GAN.
• Score: 57.6150859067392
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative Adversarial Networks (GANs) with high computation costs, e.g., BigGAN and StyleGAN2, have achieved remarkable results in synthesizing high-resolution images from random noise. Reducing the computation cost of GANs while keeping generating photo-realistic images is a challenging field. 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 about both real images and fake images, we merely transfer the knowledge from the teacher discriminator via the adversarial interaction between the teacher discriminator and the student generator. 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 and BigGAN. 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}

Related papers

• GLeaD: Improving GANs with A Generator-Leading Task [44.14659523033865]
  Generative adversarial network (GAN) is formulated as a two-player game between a generator (G) and a discriminator (D) We propose a new paradigm for adversarial training, which makes G assign a task to D as well.
  arXiv  Detail & Related papers  (2022-12-07T16:25:19Z)
• Distilling Representations from GAN Generator via Squeeze and Span [55.76208869775715]
  We propose to distill knowledge from GAN generators by squeezing and spanning their representations. We span the distilled representation of the synthetic domain to the real domain by also using real training data to remedy the mode collapse of GANs.
  arXiv  Detail & Related papers  (2022-11-06T01:10:28Z)
• Rebooting ACGAN: Auxiliary Classifier GANs with Stable Training [45.70113212633225]
  Conditional Generative Adversarial Networks (cGAN) generate realistic images by incorporating class information into GAN. One of the most popular cGANs is an auxiliary classifier GAN with softmax cross-entropy loss (ACGAN) ACGAN also tends to generate easily classifiable samples with a lack of diversity.
  arXiv  Detail & Related papers  (2021-11-01T17:51:33Z)
• Online Multi-Granularity Distillation for GAN Compression [17.114017187236836]
  Generative Adversarial Networks (GANs) have witnessed prevailing success in yielding outstanding images. GANs are burdensome to deploy on resource-constrained devices due to ponderous computational costs and hulking memory usage. We propose a novel online multi-granularity distillation scheme to obtain lightweight GANs.
  arXiv  Detail & Related papers  (2021-08-16T05:49:50Z)
• GMM-Based Generative Adversarial Encoder Learning [39.57350884615545]
  We present a simple architectural setup that combines the generative capabilities of GAN with an encoder. We model the output of the encoder latent space via a GMM, which leads to both good clustering using this latent space and improved image generation by the GAN.
  arXiv  Detail & Related papers  (2020-12-08T16:12:16Z)
• Learning Efficient GANs for Image Translation via Differentiable Masks and co-Attention Distillation [130.30465659190773]
  Generative Adversarial Networks (GANs) have been widely-used in image translation, but their high computation and storage costs impede the deployment on mobile devices. We introduce a novel GAN compression method, termed DMAD, by proposing a Differentiable Mask and a co-Attention Distillation. Experiments show DMAD can reduce the Multiply Accumulate Operations (MACs) of CycleGAN by 13x and that of Pix2Pix by 4x while retaining a comparable performance against the full model.
  arXiv  Detail & Related papers  (2020-11-17T02:39:19Z)
• TinyGAN: Distilling BigGAN for Conditional Image Generation [2.8072597424460466]
  BigGAN has significantly improved the quality of image generation on ImageNet, but it requires a huge model, making it hard to deploy on resource-constrained devices. We propose a black-box knowledge distillation framework for compressing GANs, which highlights a stable and efficient training process. Given BigGAN as the teacher network, we manage to train a much smaller student network to mimic its functionality, achieving competitive performance on Inception and FID scores with the generator having $16times$ fewer parameters.
  arXiv  Detail & Related papers  (2020-09-29T07:33:49Z)
• AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks [98.71508718214935]
  Existing GAN compression algorithms are limited to handling specific GAN architectures and losses. Inspired by the recent success of AutoML in deep compression, we introduce AutoML to GAN compression and develop an AutoGAN-Distiller framework. We evaluate AGD in two representative GAN tasks: image translation and super resolution.
  arXiv  Detail & Related papers  (2020-06-15T07:56:24Z)
• Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANs [104.85633684716296]
  We show that simple fine-tuning of GANs with frozen lower layers of the discriminator performs surprisingly well. This simple baseline, FreezeD, significantly outperforms previous techniques used in both unconditional and conditional GANs.
  arXiv  Detail & Related papers  (2020-02-25T15:30:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.