[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117645-en":3,"doc-seo-117645-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117645,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Unsupervised Panoptic Interpretation of Latent Spaces in GANs Using Space-Filling Vector Quantization","Generative adversarial networks (GANs) learn latent spaces that can produce realistic images, yet the latent-to-image mapping is hard to interpret in terms of underlying generative factors. The work introduces space-filling vector quantization (SFVQ), a modification of vector quantization that quantizes data on a piece-wise linear curve to reveal morphological structure. Applied to pretrained StyleGAN2 and BigGAN, the SFVQ curve yields a general interpretable model that assigns specific regions to generative factors and supports controllable image transformation and data augmentation.","This is an electronic reprint of the original article.  \nThis reprint may differ from the original in pagination and typographic detail.  \nVali, Mohammad Hassan; Bäckström, Tom  \nUnsupervised Panoptic Interpretation of Latent Spaces in GANs Using Space-Filling Vector Quantization  \nPublished in:  \nTransactions on Machine Learning Research  \nPublished: 01/01/2025  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nPublished under the following license:  \nCC BY  \nPlease cite the original version:  \nVali, M. H. , & Bäckström, T. (2025) . Unsupervised Panoptic Interpretation of Latent Spaces in GANs Using Space-Filling Vector Quantization. Transactions on Machine Learning Research, 2025-June. [https://openreview.net/forum?id=SEJatSGZX8](https://openreview.net/forum?id=SEJatSGZX8)  \nThis material is protected by copyright and other intellectual property rights, and duplication or sale of all or part of any of the repository collections is not permitted, except that material may be duplicated by you foryour research use or educational purposes in electronic or print form. You must obtain permission for anyother use. Electronic or print copies may not be offered, whether for sale or otherwise to anyone who is not an authorised user.  \nUnsupervised Panoptic Interpretation of Latent Spaces in GANs Using Space-Filling Vector Quantization  \nMohammad Hassan Vali  \nDepartment of Computer Science, Aalto University  \nTom Bäckström  \nDepartment of Information and Communications Engineering, Aalto University  \n[mohammad.vali@aalto.fi](mohammad.vali@aalto.fi)  \n[tom. backstrom@aalto.fi](tom. backstrom@aalto.fi)  \nReviewed on OpenReview: [https: // openreview. net/ forum? id= SEJatSGZX8](https: // openreview. net/ forum? id= SEJatSGZX8)  \nAbstract  \nGenerative adversarial networks (GANs) learn a latent space whose samples can be mapped to real-world images. Such latent spaces are difficult to interpret. Some earlier supervised methods aim to create an interpretable latent space or discover interpretable directions, which requires exploiting data labels or annotated synthesized samples for training. However, we propose using a modification of vector quantization called space-filling vector quantization (SFVQ), which quantizes the data on a piece-wise linear curve. SFVQ can capture the underlying morphological structure of the latent space, making it interpretable. We apply this technique to model the latent space of pre-trained StyleGAN2 and BigGAN networks on various datasets. Our experiments show that the SFVQ curve yields a general interpretable model of the latent space such that it determines which parts of the latent space correspond to specific generative factors. Furthermore, we demonstrate that each line of the SFVQ curve can potentially refer to an interpretable direction for applying intelligible image transformations. We also demonstrate that the points located on an SFVQ line can be used for controllable data augmentation.  \n1 Introduction  \nGenerative adversarial networks (GANs) (Goodfellow et al. , 2014) are powerful generative models applied to various applications, e.g., data augmentation (Antoniou et al., 2017; Shorten & Khoshgoftaar, 2019), image editing (Härkönen et al., 2020; Yüksel et al. , 2021; Shen & Zhou, 2021; Voynov & Babenko, 2020; Tzelepis et al., 2021; Aoshima & Matsubara, 2023; Abdal et al., 2021; Wang et al., 2018b; Alaluf et al., 2022; Roich et al. , 2022; Pehlivan et al., 2023; Liu et al., 2023; Jahanian et al. , 2019; Plumerault et al., 2020; Yang et al., 2021; Goetschalckx et al., 2019; Shen et al., 2020; Wu et al., 2021), and video generation (Wang et al., 2018a) . For image data, GANs map a latent space to an output image space by learning a non-linear mapping (Voynov & Babenko, 2020) . After learning such mapping, GANs can create realistic high-resolution images by sampling from the latent space (Karras et al., 2019) . However, this latent space is a black box, making it difficult to int","cbCainIoLyPsWucw","https://ap.wps.com/l/cbCainIoLyPsWucw","pdf",46028835,1,32,"English","en",105,"# Introduction\n## Interpreting GAN latent spaces\n## Supervised vs. unsupervised methods\n## Limitations of existing approaches","[{\"question\":\"What problem does the paper address regarding GAN latent spaces?\",\"answer\":\"GAN latent spaces are difficult to interpret because the mapping from latent variables to generative factors (e.g., attributes like gender and age) is not directly understood, and useful interpretable directions are often unknown.\"},{\"question\":\"How does SFVQ differ from standard vector quantization?\",\"answer\":\"SFVQ modifies vector quantization by quantizing data along a piece-wise linear, space-filling curve, enabling it to capture morphological structure of the latent space and make it more interpretable.\"},{\"question\":\"What do the authors show using pretrained StyleGAN2 and BigGAN?\",\"answer\":\"They show that the SFVQ curve provides an interpretable model where different parts of the curve correspond to specific generative factors, and the curve’s lines can serve as interpretable directions for image transformations and controllable data augmentation.\"}]","Unsupervised Panoptic Interpretation of Latent Spaces in GANs Using Space-Filling Vector Quantization | 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problem does the paper address regarding GAN latent spaces?","Question",{"text":75,"@type":76},"GAN latent spaces are difficult to interpret because the mapping from latent variables to generative factors (e.g., attributes like gender and age) is not directly understood, and useful interpretable directions are often unknown.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does SFVQ differ from standard vector quantization?",{"text":80,"@type":76},"SFVQ modifies vector quantization by quantizing data along a piece-wise linear, space-filling curve, enabling it to capture morphological structure of the latent space and make it more interpretable.",{"name":82,"@type":73,"acceptedAnswer":83},"What do the authors show using pretrained StyleGAN2 and BigGAN?",{"text":84,"@type":76},"They show that the SFVQ curve provides an interpretable model where different parts of the curve correspond to specific generative factors, and the curve’s lines can serve as interpretable directions for 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