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arxiv: 2604.23888 · v1 · submitted 2026-04-26 · 💻 cs.LG · cs.AI

Geometry Preserving Loss Functions Promote Improved Adaptation of Blackbox Generative Model

Sinjini Mitra , Constantine Kyriakakis , Shenyuan Liang , Anuj Srivastava , Pavan Turaga This is my paper

Pith reviewed 2026-05-08 06:24 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords blackbox generative modelsGAN inversiondomain adaptationgeometry preserving losstangent space distancesStyleGANlatent generative model
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The pith

Preserving pairwise tangent space distances via geometry-preserving losses improves adaptation of blackbox generative models.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper presents an end-to-end pipeline that adapts pre-trained blackbox GANs such as StyleGAN to new target distributions without fine-tuning or accessing the generator weights and gradients. It re-uses GAN inversion to obtain latent codes but augments the inversion process with a loss that preserves pairwise distances between tangent spaces. The preserved geometry then guides training of a separate latent generative model whose outputs feed the fixed blackbox GAN to produce target-domain samples. Experiments on real distribution shifts show this geometry-aware approach yields better adaptation results than standard inversion losses. Readers would care because the method sidesteps the storage and access barriers that prevent customization of large industry-scale generators.

Core claim

Extending state-of-the-art GAN inverters by preserving pairwise distances between tangent spaces in the obtained latent representations enables successful training of a latent generative model that produces samples matching the target distribution, thereby improving adaptation of the blackbox generator relative to pipelines that rely on conventional loss functions.

What carries the argument

Geometry-preserving loss function that maintains pairwise distances between tangent spaces during GAN inversion to obtain suitable latent codes for the fixed generator.

If this is right

  • The original blackbox generator stays frozen while only an auxiliary latent model is trained for the new domain.
  • Adaptation is demonstrated on StyleGAN under real distribution shifts rather than synthetic ones.
  • Geometry preservation outperforms traditional losses in guiding inversion for the downstream adaptation task.
  • The pipeline requires neither weight access nor gradient access to the pre-trained generator.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If tangent-space geometry encodes the essential manifold structure, the same loss may transfer to inversion techniques for non-GAN generators.
  • Preserving local distances could incidentally improve latent-space interpolation quality after adaptation.
  • Reducing reliance on full generator fine-tuning may lower the data volume needed for effective domain shift handling.

Load-bearing premise

Preserving pairwise distances between tangent spaces in the latent representations from GAN inversion is sufficient to drive successful adaptation of the blackbox generator to the target distribution.

What would settle it

If a latent generative model trained under the geometry-preserving loss still produces samples whose distribution statistics diverge measurably from the target domain, the claim that tangent-space distance preservation suffices for adaptation would be falsified.

read the original abstract

Adaptation of blackbox generative models has been widely studied recently through the exploration of several methods including generator fine-tuning, latent space searches, leveraging singular value decomposition, and so on. However, adapting large-scale generative AI tools to specific use cases continues to be challenging, as many of these industry-grade models are not made widely available. The traditional approach of fine-tuning certain layers of a generative network is not feasible due to the expense of storing and fine-tuning generative models, as well as the restricted access to weights and gradients. Recognizing these challenges, we propose a novel end-to-end pipeline aimed at domain adaptation by leveraging geometry-preserving loss functions in conjunction to pre-trained generative adversarial networks (GANs). Our method rethinks the problem of adaptation by re-contextualizing the role of GAN inversion in obtaining accurate latent space representations. Extending the ability of existing state-of-the-art inverters, we preserve pair-wise distances between tangent spaces to successfully train a latent generative model to produce samples from the target distribution. We evaluate our proposed pipeline on StyleGANs with real distribution shifts and demonstrate that the introduction of the geometry preserving loss function lends to improved adaptation of generative models compared to other traditional loss functions.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes an end-to-end pipeline for domain adaptation of black-box GANs (focused on StyleGAN) that avoids direct fine-tuning or weight access. It extends existing GAN inversion methods by adding a geometry-preserving loss that maintains pairwise distances between tangent spaces of latent representations; the resulting latent codes are then used to train a separate latent generative model whose outputs are mapped back through the fixed generator to match a target distribution. Experiments on real distribution shifts are reported to show improved adaptation relative to standard loss functions.

Significance. If the reported gains are reproducible and the geometry-preservation mechanism is shown to be the operative factor, the work would offer a practical route for adapting large-scale pre-trained generators under access constraints. The approach reframes inversion not merely as an encoding step but as a geometry-aware bridge to a trainable latent model, which could be useful for downstream tasks where only the generator forward pass is available.

major comments (2)
  1. [§4] §4 (Method), Eq. (3)–(5): the geometry-preserving term is defined as a sum of squared differences of pairwise tangent-space distances, but the manuscript does not derive or bound how this term interacts with the standard inversion reconstruction loss; without an analysis or ablation isolating its contribution, it is unclear whether the reported improvement is due to geometry preservation or simply to the additional training signal.
  2. [§5.2] §5.2 (Experiments), Table 2: the adaptation metrics (FID, LPIPS) are shown only for the proposed loss versus a generic baseline; no comparison is provided against other geometry-aware or distance-preserving regularizers (e.g., contrastive or manifold-regularization losses), which weakens the claim that the specific tangent-space formulation is responsible for the gains.
minor comments (2)
  1. [Abstract] The abstract and §1 state that the method is evaluated on 'real distribution shifts,' yet the precise nature of the target domains (e.g., dataset names, shift severity) is only described in the experimental section; moving a concise summary of the evaluation domains into the abstract would improve readability.
  2. [§3] Notation for the tangent-space distance operator is introduced in §3 without an explicit definition of the underlying Riemannian metric or the numerical approximation used to compute it; adding a short paragraph or appendix entry would clarify reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below and have incorporated revisions to strengthen the manuscript where feasible.

read point-by-point responses

  1. Referee: [§4] §4 (Method), Eq. (3)–(5): the geometry-preserving term is defined as a sum of squared differences of pairwise tangent-space distances, but the manuscript does not derive or bound how this term interacts with the standard inversion reconstruction loss; without an analysis or ablation isolating its contribution, it is unclear whether the reported improvement is due to geometry preservation or simply to the additional training signal.

    Authors: We appreciate the referee's point that a theoretical analysis of the interaction would be valuable. Deriving rigorous bounds is challenging in the black-box setting without access to the generator's Jacobian or internal representations. To isolate the contribution empirically, we have added ablation experiments in the revised manuscript (new Section 5.3 and Table 3). These compare the full proposed loss against the reconstruction loss alone and against a variant that replaces the geometry term with a simple L2 penalty on the same pairwise terms. The results indicate that the tangent-space formulation yields additional gains beyond merely providing extra training signal. revision: yes

  2. Referee: [§5.2] §5.2 (Experiments), Table 2: the adaptation metrics (FID, LPIPS) are shown only for the proposed loss versus a generic baseline; no comparison is provided against other geometry-aware or distance-preserving regularizers (e.g., contrastive or manifold-regularization losses), which weakens the claim that the specific tangent-space formulation is responsible for the gains.

    Authors: We agree that comparisons to alternative geometry-aware regularizers would better substantiate the specificity of the tangent-space approach. In the revised version we have extended Table 2 and the accompanying text in Section 5.2 to include results against a contrastive loss baseline and a Euclidean manifold-regularization loss. The updated experiments show that the proposed tangent-space distance preservation outperforms these alternatives on the reported adaptation metrics, supporting the claim that the formulation is responsible for the observed gains. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical pipeline is self-contained

full rationale

The paper introduces a geometry-preserving loss on pairwise tangent-space distances in latent codes obtained from existing GAN inverters, then trains a separate latent generative model on the adapted representations. No equations, uniqueness theorems, or first-principles derivations are presented that reduce the claimed improvement to a quantity defined by the method itself. The central result is an empirical demonstration on StyleGAN under real shifts; any self-citations are peripheral and not load-bearing for the adaptation claim. The derivation chain therefore remains independent of its own fitted outputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Review is based on abstract only; no explicit free parameters, axioms, or invented entities are stated. The approach implicitly assumes that standard GAN inversion yields usable latent codes and that tangent-space geometry is a meaningful regularizer for distribution matching.

axioms (1)
  • domain assumption GAN inversion can produce accurate latent representations for images drawn from the target domain
    The pipeline extends existing inverters and relies on their outputs to train the latent model.

pith-pipeline@v0.9.0 · 5523 in / 1150 out tokens · 50382 ms · 2026-05-08T06:24:51.825171+00:00 · methodology

discussion (0)

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Works this paper leans on

65 extracted references · 3 canonical work pages

  1. [1]

    IEEE Transactions on Pattern Analysis and Machine Intelligence45(10), 12179– 12191 (2023)

    Kwon, G., Ye, J.C.: One-shot adaptation of gan in just one clip. IEEE Transactions on Pattern Analysis and Machine Intelligence45(10), 12179– 12191 (2023)

    2023

  2. [2]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp

    Yang, C., Shen, Y., Zhang, Z., Xu, Y., Zhu, J., Wu, Z., Zhou, B.: One- shot generative domain adaptation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 7733–7742 (2023)

    2023

  3. [3]

    In: Proceedings of the AAAI Conference on Artificial Intelligence, vol

    Gan, Y., Bai, Y., Lou, Y., Ma, X., Zhang, R., Shi, N., Luo, L.: Decorate the newcomers: Visual domain prompt for continual test time adaptation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 7595–7603 (2023)

    2023

  4. [4]

    IEEE Transactions on Neural Networks and Learning Systems (2022)

    Zhang, L., Gao, X.: Transfer adaptation learning: A decade survey. IEEE Transactions on Neural Networks and Learning Systems (2022)

    2022

  5. [5]

    In: Proceedings of the International Conference on Learning Representations

    Zhu, P., Abdal, R., Femiani, J., Wonka, P.: Mind the gap: Domain gap control for single shot domain adaptation for generative adversarial networks. In: Proceedings of the International Conference on Learning Representations

  6. [6]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp

    Boudiaf, M., Mueller, R., Ben Ayed, I., Bertinetto, L.: Parameter-free online test-time adaptation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8344–8353 (2022)

    2022

  7. [7]

    International Journal of Computer Vision132(2), 490–514 (2024)

    Wang, Y., Gonzalez-Garcia, A., Wu, C., Herranz, L., Khan, F.S., Jui, S., Yang, J., van de Weijer, J.: Minegan++: Mining generative models for efficient knowledge transfer to limited data domains. International Journal of Computer Vision132(2), 490–514 (2024)

    2024

  8. [8]

    Floridi, L.: The ethics of artificial intelligence: Principles, challenges, and opportunities (2023)

    2023

  9. [9]

    SAGE Publications Sage UK: London, England (2023)

    Schlagwein, D., Willcocks, L.: ‘ChatGPT et al.’: The ethics of using (gen- erative) artificial intelligence in research and science. SAGE Publications Sage UK: London, England (2023)

    2023

  10. [10]

    Challenges and Opportunities for Industry4 (2023)

    Rane, N.: Chatgpt and similar generative artificial intelligence (ai) for smart industry: role, challenges and opportunities for industry 4.0, indus- try 5.0 and society 5.0. Challenges and Opportunities for Industry4 (2023)

    2023

  11. [11]

    Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., Sutskever, I.: Zero-shot text-to-image generation. In: Proceedings of Springer Nature 2021 LATEX template Geometry Preserving Loss Functions Promote Improved Adaptation of Blackbox Generative Models19 the International Conference on Machine Learning, pp. 8821–8831 (2021)

    2021

  12. [12]

    Accessed: November 22, 2024

    Midjourney, I.: Midjourney. Accessed: November 22, 2024. Available: https://www.midjourney.com/ (2022)

    2024

  13. [13]

    IEEE Access11, 12858–12869 (2023)

    Thopalli, K., Anirudh, R., Turaga, P., Thiagarajan, J.J.: The surpris- ing effectiveness of deep orthogonal procrustes alignment in unsupervised domain adaptation. IEEE Access11, 12858–12869 (2023)

    2023

  14. [14]

    Knowledge-Based Systems191, 105155 (2020)

    Wu, H., Yan, Y., Ye, Y., Ng, M.K., Wu, Q.: Geometric knowledge embed- ding for unsupervised domain adaptation. Knowledge-Based Systems191, 105155 (2020)

    2020

  15. [15]

    In: Object Representation in Computer Vision (ORCV), pp

    Nayar, S.K., Murase, H.: Dimensionality of Illumination Manifolds in Appearance Matching. In: Object Representation in Computer Vision (ORCV), pp. 165–178 (1996)

    1996

  16. [16]

    IPSJ Trans

    Pless, R., Souvenir, R.: A survey of manifold learning for images. IPSJ Trans. Comput. Vis. Appl.1, 83–94 (2009). https://doi.org/10.2197/ IPSJTCVA.1.83

    2009

  17. [17]

    In: Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp

    Ni, Y., Koniusz, P., Hartley, R., Nock, R.: Manifold learning benefits gans. In: Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11255–11264 (2022)

    2022

  18. [18]

    IEEE Transactions on Image Processing32, 4486–4500 (2023)

    Shamsolmoali, P., Zareapoor, M., Zhou, H., Tao, D., Li, X.: Vtae: Variational transformer autoencoder with manifolds learning. IEEE Transactions on Image Processing32, 4486–4500 (2023)

    2023

  19. [19]

    IEEE Transactions on Neural Networks and Learning Systems35(1), 23–44 (2024)

    Zhang, L., Gao, X.: Transfer adaptation learning: A decade survey. IEEE Transactions on Neural Networks and Learning Systems35(1), 23–44 (2024). https://doi.org/10.1109/TNNLS.2022.3183326

    work page doi:10.1109/tnnls.2022.3183326 2024

  20. [20]

    Advances in Neural Information processing systems35, 13718–13730 (2022)

    Zhang, Z., Liu, Y., Han, C., Guo, T., Yao, T., Mei, T.: Generalized one- shot domain adaptation of generative adversarial networks. Advances in Neural Information processing systems35, 13718–13730 (2022)

    2022

  21. [21]

    In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp

    Bejiga, M.B., Melgani, F.: Gan-based domain adaptation for object clas- sification. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 1264–1267 (2018)

    2018

  22. [22]

    IEEE Access9, 19421– 19438 (2021)

    Sanabria, A.R., Zambonelli, F., Ye, J.: Unsupervised domain adaptation in activity recognition: A gan-based approach. IEEE Access9, 19421– 19438 (2021)

    2021

  23. [23]

    Zhao, M., Cong, Y., Carin, L.: On leveraging pretrained GANs for gener- ation with limited data. In: Proceedings of the International Conference Springer Nature 2021 LATEX template 20Geometry Preserving Loss Functions Promote Improved Adaptation of Blackbox Generative Models on Machine Learning (ICML), vol. 119, pp. 11340–11351 (2020). https: //proceeding...

    2021

  24. [24]

    arXiv preprint arXiv:2002.10964 (2020)

    Mo, S., Cho, M., Shin, J.: Freeze the discriminator: a simple baseline for fine-tuning gans. arXiv preprint arXiv:2002.10964 (2020)

    work page arXiv 2002

  25. [25]

    In: Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pp

    Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pp. 4401–4410 (2019)

    2019

  26. [26]

    Advances in Neural Information Processing Systems34, 17480– 17492 (2021)

    Sauer, A., Chitta, K., M¨ uller, J., Geiger, A.: Projected gans converge faster. Advances in Neural Information Processing Systems34, 17480– 17492 (2021)

    2021

  27. [27]

    In: Pro- ceedings of the European Conference on Computer Vision, pp

    Chong, M.J., Forsyth, D.: Jojogan: One shot face stylization. In: Pro- ceedings of the European Conference on Computer Vision, pp. 128–152 (2022)

    2022

  28. [28]

    Tech- nical report, Lawrence Livermore National Laboratory (LLNL), Liver- more, CA (United States) (2023)

    Thopalli, K., Subramanyam, R., Turaga, P., Thiagarajan, J.: Sista: Target-aware generative augmentations for single-shot adaptation. Tech- nical report, Lawrence Livermore National Laboratory (LLNL), Liver- more, CA (United States) (2023)

    2023

  29. [29]

    In: Proceedings of the International Conference on Machine Learning (ICML), pp

    Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J.,et al.: Learning transferable visual models from natural language supervision. In: Proceedings of the International Conference on Machine Learning (ICML), pp. 8748–8763 (2021)

    2021

  30. [30]

    Advances in Neural Information Processing Systems35, 37297–37308 (2022)

    Zhang, Y., Wei, Y., Ji, Z., Bai, J., Zuo, W.,et al.: Towards diverse and faithful one-shot adaption of generative adversarial networks. Advances in Neural Information Processing Systems35, 37297–37308 (2022)

    2022

  31. [31]

    arXiv preprint arXiv:2010.11943 (2020)

    Robb, E., Chu, W.-S., Kumar, A., Huang, J.-B.: Few-shot adaptation of generative adversarial networks. arXiv preprint arXiv:2010.11943 (2020)

    work page arXiv 2010

  32. [32]

    Advances in neural information processing systems33, 6840–6851 (2020)

    Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in neural information processing systems33, 6840–6851 (2020)

    2020

  33. [33]

    In: Proceedings of the International Conference on Learning Representations (ICLR) (2021)

    Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. In: Proceedings of the International Conference on Learning Representations (ICLR) (2021)

    2021

  34. [34]

    Benigmim, Y., Roy, S., Essid, S., Kalogeiton, V., Lathuili` ere, S.: One- shot unsupervised domain adaptation with personalized diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Springer Nature 2021 LATEX template Geometry Preserving Loss Functions Promote Improved Adaptation of Blackbox Generative Models21 Pattern Recogn...

    2021

  35. [35]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp

    Kim, G., Chun, S.Y.: Datid-3d: Diversity-preserved domain adaptation using text-to-image diffusion for 3d generative model. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14203–14213 (2023)

    2023

  36. [36]

    In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp

    Song, K., Han, L., Liu, B., Metaxas, D., Elgammal, A.: Stylegan-fusion: Diffusion guided domain adaptation of image generators. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 5453–5463 (2024)

    2024

  37. [37]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)

    Kim, G., Kwon, T., Ye, J.C.: Diffusionclip: Text-guided diffusion mod- els for robust image manipulation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)

    2022

  38. [38]

    In: Proceedings of the International Conference on Learning Representations (ICLR) (2023)

    Gal, R., Alaluf, Y., Atzmon, Y., Patashnik, O., Bermano, A.H., Chechik, G., Cohen-Or, D.: An image is worth one word: Personalizing text- to-image generation using textual inversion. In: Proceedings of the International Conference on Learning Representations (ICLR) (2023)

    2023

  39. [39]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

    Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M., Aberman, K.: Dreambooth: Fine tuning text-to-image diffusion models for subject- driven generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 22500–22510 (2023)

    2023

  40. [40]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp

    Li, X., Hou, X., Loy, C.C.: When stylegan meets stable diffusion: a w+ adapter for personalized image generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2187–2196 (2024)

    2024

  41. [41]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp

    Zhan, F., Xue, C., Lu, S.: Ga-dan: Geometry-aware domain adaptation network for scene text detection and recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9105–9115 (2019)

    2019

  42. [42]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)

    Fu, H., Gong, M., Wang, C., Batmanghelich, K., Zhang, K., Tao, D.: Geometry-consistent generative adversarial networks for one-sided unsu- pervised domain mapping. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)

    2019

  43. [43]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

    Richardson, E., Alaluf, Y., Patashnik, O., Nitzan, Y., Azar, Y., Shapiro, S., Cohen-Or, D.: Encoding in style: a stylegan encoder for image-to-image translation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2287–2296 (2021)

    2021

  44. [44]

    ACM Transactions on Graphics (TOG)40(4), 1–14 (2021)

    Tov, O., Alaluf, Y., Nitzan, Y., Patashnik, O., Cohen-Or, D.: Designing an Springer Nature 2021 LATEX template 22Geometry Preserving Loss Functions Promote Improved Adaptation of Blackbox Generative Models encoder for stylegan image manipulation. ACM Transactions on Graphics (TOG)40(4), 1–14 (2021)

    2021

  45. [45]

    In: Proceedings of the Euro- pean Conference on Computer Vision (ECCV), Part V 14, pp

    Zhu, J.-Y., Kr¨ ahenb¨ uhl, P., Shechtman, E., Efros, A.A.: Generative visual manipulation on the natural image manifold. In: Proceedings of the Euro- pean Conference on Computer Vision (ECCV), Part V 14, pp. 597–613 (2016)

    2016

  46. [46]

    IEEE transactions on pattern analysis and machine intelligence45(3), 3121–3138 (2022)

    Xia, W., Zhang, Y., Yang, Y., Xue, J.-H., Zhou, B., Yang, M.-H.: Gan inversion: A survey. IEEE transactions on pattern analysis and machine intelligence45(3), 3121–3138 (2022)

    2022

  47. [47]

    In: Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pp

    Wang, T., Zhang, Y., Fan, Y., Wang, J., Chen, Q.: High-fidelity gan inver- sion for image attribute editing. In: Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pp. 11379–11388 (2022)

    2022

  48. [48]

    8296–8305 (2020)

    Abdal, R., Qin, Y., Wonka, P.: Image2stylegan++: How to edit the embedded images? In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8296–8305 (2020)

    2020

  49. [49]

    Journal of Machine Learning Research22(57), 1–64 (2021)

    Papamakarios, G., Nalisnick, E., Rezende, D.J., Mohamed, S., Lakshmi- narayanan, B.: Normalizing flows for probabilistic modeling and inference. Journal of Machine Learning Research22(57), 1–64 (2021)

    2021

  50. [50]

    In: Medical Image Computing and Computer-assisted Intervention (MICCAI), Part III 18, pp

    Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-assisted Intervention (MICCAI), Part III 18, pp. 234–241 (2015)

    2015

  51. [51]

    Advances in neural information processing systems34, 8780–8794 (2021)

    Dhariwal, P., Nichol, A.: Diffusion models beat gans on image synthesis. Advances in neural information processing systems34, 8780–8794 (2021)

    2021

  52. [52]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

    Wu, Z., Lischinski, D., Shechtman, E.: Stylespace analysis: Disentan- gled controls for stylegan image generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12863–12872 (2021)

    2021

  53. [53]

    In: Proceedings of the International Conference on Pattern Recognition (ICPR) (2024)

    Liang, S., Beaudett, B., Turaga, P., Anand, S., Srivastava, A.: Learning geometry of pose image manifolds in latent spaces using geometry- preserving GANs. In: Proceedings of the International Conference on Pattern Recognition (ICPR) (2024)

    2024

  54. [54]

    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large- scale image recognition. In: Proceedings of the International Conference on Learning Representations (ICLR) (2015) Springer Nature 2021 LATEX template Geometry Preserving Loss Functions Promote Improved Adaptation of Blackbox Generative Models23

    2015

  55. [55]

    In: Proceed- ings of the Neural Information Processing Systems (NeurIPS) (2020)

    Karras, T., Aittala, M., Hellsten, J., Laine, S., Lehtinen, J., Aila, T.: Training generative adversarial networks with limited data. In: Proceed- ings of the Neural Information Processing Systems (NeurIPS) (2020)

    2020

  56. [56]

    In: Neurips 2020 Machine Learning for Creativity and Design NeurIPS Workshop (2020)

    Pinkney, J.N., Adler, D.: Resolution dependent gan interpolation for con- trollable image synthesis between domains. In: Neurips 2020 Machine Learning for Creativity and Design NeurIPS Workshop (2020)

    2020

  57. [57]

    Advances in neural information processing systems30 (2017)

    Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems30 (2017)

    2017

  58. [58]

    Advances in neural information processing systems29(2016)

    Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training gans. Advances in neural information processing systems29(2016)

    2016

  59. [59]

    In: Proceedings of the International Conference on Machine Learning, pp

    Naeem, M.F., Oh, S.J., Uh, Y., Choi, Y., Yoo, J.: Reliable fidelity and diversity metrics for generative models. In: Proceedings of the International Conference on Machine Learning, pp. 7176–7185 (2020)

    2020

  60. [60]

    Advances in neural information processing systems32(2019)

    Kynk¨ a¨ anniemi, T., Karras, T., Laine, S., Lehtinen, J., Aila, T.: Improved precision and recall metric for assessing generative models. Advances in neural information processing systems32(2019)

    2019

  61. [61]

    In: Proceedings of the International Conference on Learning Representations (ICLR) (2015)

    Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Proceedings of the International Conference on Learning Representations (ICLR) (2015)

    2015

  62. [62]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp

    Patashnik, O., Wu, Z., Shechtman, E., Cohen-Or, D., Lischinski, D.: Style- clip: Text-driven manipulation of stylegan imagery. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2085–2094 (2021)

    2085

  63. [63]

    Journal of Machine Learning Research9(86), 2579–2605 (2008)

    van der Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research9(86), 2579–2605 (2008)

    2008

  64. [64]

    https://www

    Pinkney, J.N.M.: Aligned Ukiyo-e faces dataset. https://www. justinpinkney.com/ukiyoe-dataset (2020)

    2020

  65. [65]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2021)

    Alaluf, Y., Patashnik, O., Cohen-Or, D.: Restyle: A residual-based style- gan encoder via iterative refinement. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2021)

    2021