variation. We also compare with DIP [38], which uses a discriminative model as prior, and Zhang et al. Google allows users to search the Web for images, news, products, video, and other content. A large number of articles published around GAN were published in major journals and conferences to improve and analyze GAN's mathematical research, improve GAN's generation quality research, GAN's application in image generation, and GAN's application in NLP and other fields. Bala, and Kilian Weinberger. With such composition, the reconstructed image can be generated with, where ⊙ denotes the channel-wise multiplication as. Hence, such high-level knowledge from these models cannot be reused. Finally, we provide more inversion results for both PGGAN [23] and StyleGAN [24] in Sec.C, as well as more application results in Sec.D. After inversion, we apply the reconstruction result as the multi-code GAN prior to a variety of image processing tasks. With the high-fidelity image reconstruction, our multi-code inversion method facilitates many image processing tasks with pre-trained GANs as prior. CVPR 2020 • Jinjin Gu • Yujun Shen • Bolei Zhou. A style-based generator architecture for generative adversarial We summarize our contributions as follows: We propose an effective GAN inversion method by using multiple latent codes and adaptive channel importance. Besides PSNR and LPIPS, we introduce Naturalness Image Quality Evaluator (NIQE) as an extra metric. Courville. In this section, we show more inversion results of our method on PGGAN [23] and StyleGAN [24]. We do so by log probability term. ∙ Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Therefore, we introduce the way we cast seis-mic image processing problem in the CNN framework, Richard Zhang, Phillip Isola, and Alexei A Efros. On the contrary, the over-parameterization design of using multiple latent codes enhances the stability. David Bau, Jun-Yan Zhu, Hendrik Strobelt, Bolei Zhou, Joshua B. Tenenbaum, In other words, the expressiveness of using a single latent code is limited by the finite code dimensionality. As pointed out by prior work [21, 15, 34], GANs have already encoded some interpretable semantics inside the latent space. Fig.5 includes some examples of restoring corrupted images. In the following, we introduce how to utilize multiple latent codes for GAN inversion. Global guarantees for enforcing deep generative priors by empirical As shown in Fig.8, we successfully exchange styles from different levels between source and target images, suggesting that our inversion method can well recover the input image with respect to different levels of semantics. We [39] inverted a discriminative model, starting from deep convolutional features, to achieve semantic image transformation. Such prior can be inversely used for image generation and image reconstruction [39, 38, 2]. Bau et al. Lore Goetschalckx, Alex Andonian, Aude Oliva, and Phillip Isola. Jeff Donahue, Philipp Krähenbühl, and Trevor Darrell. Deep feature interpolation for image content changes. We can rank the concepts related to each latent code with IoUzn,c and label each latent code with the concept that matches best. Furthermore, GANs are especially useful for controllable generation since their latent spaces contain a wide range of interpretable directions, well suited for semantic editing operations. Fig.12 shows the comparison results. share. A GAN is a generative model that is trained using two neural network models. For each application, the GAN model is fixed without retraining. where ∘ denotes the element-wise product. Jingwen Chen, Jiawei Chen, Hongyang Chao, and Ming Yang. We apply the inverted results as the multi-code GAN prior to a range of real-world applications, such as image colorization, super-resolution, image inpainting, semantic manipulation, etc, demonstrating its potential in real image processing. (a) optimizing a single latent code z as in Eq. Faceid-gan: Learning a symmetry three-player gan for. In a discriminative model, the loss measures the accuracy of the prediction and we use it to monitor the progress of the training. such as 256x256 pixels) and the capability of performing well on … Raymond A Yeh, Chen Chen, Teck Yian Lim, Alexander G Schwing, Mark In principle, it is impossible to recover every detail of any arbitrary real image using a single latent code, otherwise, we would have an unbeatable image compression method. Tab.4 shows the quantitative comparison, where our approach achieves the best performances on both settings of center crop and random crop. [46], which is specially designed for colorization task. learning an additional encoder. we do not control which byte in z determines the color of the hair. Guillaume Lample, Neil Zeghidour, Nicolas Usunier, Antoine Bordes, Ludovic Consequently, the reconstructed image with low quality is unable to be used for image processing tasks. Infrared image colorization based on a triplet dcgan architecture. David Bau, Hendrik Strobelt, William Peebles, Jonas Wulff, Bolei Zhou, Jun-Yan Andrew Brock, Jeff Donahue, and Karen Simonyan. Antonia Creswell and Anil Anthony Bharath. It turns out that the latent codes are specialized to invert different meaningful image regions to compose the whole image. Image blind denoising with generative adversarial network based noise That is because it only inverts the GAN model to some intermediate feature space instead of the earliest hidden space. Obviously, there is a trade-off between the dimension of optimization space and the inversion quality. metric. measurements. Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday. Gan dissection: Visualizing and understanding generative adversarial Chen Change Loy. It seems that we will soon be able to sit down and make an effort on getting this project rolling. William T. Freeman, and Antonio Torralba. Updated 4:32 pm CST, Saturday, November 28, 2020 Fangchang Ma, Ulas Ayaz, and Sertac Karaman. Dong-Wook Kim, Jae Ryun Chung, and Seung-Won Jung. Torralba. synthesis, applying trained GAN models to real image processing remains Their neural representations are shown to contain various levels of semantics underlying the observed data [21, 15, 34, 42]. Xiaodan Liang, Hao Zhang, Liang Lin, and Eric Xing. From this point, our inversion method provides a feasible way to utilize these learned semantics for real image manipulation. The experiments show that our approach significantly improves the image reconstruction quality. For this purpose, we propose In-Domain GAN inversion (IDInvert) by first training a novel domain-guided encoder which is able to produce in-domain latent code, and then performing domain-regularized optimization which involves the encoder as a regularizer to land the code inside the latent space when being finetuned. This benefits from the rich knowledge GANs have learned when trained to synthesize photo-realistic images. A recent work [3] applied generative image prior to semantic photo manipulation, but it can only edit some partial regions of the input image yet fails to apply to other tasks like colorization or super-resolution. Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, and Antonio We compare our inversion method with optimizing the intermediate feature maps [3]. layer of the generator, then compose them with adaptive channel importance to When the approximation is close enough to the input, we assume the reconstruction before post-processing is what we want. We also conduct experiments on the StyleGAN [24] model to show the reconstruction from the multi-code GAN inversion supports style mixing. 01/22/2020 ∙ by Sheng Zhong, et al. Gated-gan: Adversarial gated networks for multi-collection style invert a target image back to the latent space either by back-propagation or by For the weighted-averaging method, it manages to assign different importance scores for different latent codes so as to better recover the shape of the target image. However, due to the highly non-convex natural of this optimization problem, previous methods fail to ideally reconstruct an arbitrary image by optimizing a single latent code. Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, and Jianxiong Despite the success of Generative Adversarial Networks (GANs) in image The compound is a very hard material that has a Wurtzite crystal structure.Its wide band gap of 3.4 eV affords it special properties for applications in optoelectronic, high-power and high-frequency devices. These applications include image denoising [9, 25], image inpainting [43, 45], super-resolution [28, 41], image colorization [37, 20], style mixing [19, 10], semantic image manipulation [40, 29], etc. Compared to existing approaches, we make two major improvements by (i) employing multiple latent codes, and (ii) performing feature composition with adaptive channel importance.