mlsanityData to 7 Oct 2026

Fader Networks: Manipulating Images by Sliding Attributes

arXiv 1706.00409 ↗First posted 1 Jun 2017cs.CVcited by 118

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Abstract (arXiv)

This paper introduces a new encoder-decoder architecture that is trained to reconstruct images by disentangling the salient information of the image and the values of attributes directly in the latent space. As a result, after training, our model can generate different realistic versions of an input image by varying the attribute values. By using continuous attribute values, we can choose how much a specific attribute is perceivable in the generated image. This property could allow for applications where users can modify an image using sliding knobs, like faders on a mixing console, to change the facial expression of a portrait, or to update the color of some objects. Compared to the state-of-the-art which mostly relies on training adversarial networks in pixel space by altering attribute values at train time, our approach results in much simpler training schemes and nicely scales to multiple attributes. We present evidence that our model can significantly change the perceived value of the attributes while preserving the naturalness of images.

Key facts. Cited by 118 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Jun 2017.

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Citing papers per year: 2021: 39; 2022: 35; 2023: 19; 2024: 12; 2025: 6; 2026: 7.

118 papers.

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  1. 2022Generative Adversarial Networkscited by 5,603
  2. 2022Prompt-to-Prompt Image Editing with Cross Attention Controlcited by 2,409
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  5. 2021Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editingcited by 91
  6. 2021Learned Coarse Models for Efficient Turbulence Simulationcited by 90
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  8. 2021Driving-Signal Aware Full-Body Avatarscited by 81
  9. 2021Explainability of deep vision-based autonomous driving systems: Review and challengescited by 80
  10. 2021RAVE: A variational autoencoder for fast and high-quality neural audio synthesiscited by 72
  11. 2022Stitch it in Time: GAN-Based Facial Editing of Real Videoscited by 68
  12. 2021A Latent Transformer for Disentangled Face Editing in Images and Videoscited by 67
38 more
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  2. 2022Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolutioncited by 42
  3. 2023Counterfactual Identifiability of Bijective Causal Modelscited by 42
  4. 2021Counterfactual State Explanations for Reinforcement Learning Agents via Generative Deep Learningcited by 38
  5. 2021CoReD: Generalizing Fake Media Detection with Continual Representation using Distillationcited by 37
  6. 2022State-of-the-Art in the Architecture, Methods and Applications of StyleGANcited by 34
  7. 2021Echocardiography Segmentation with Enforced Temporal Consistencycited by 30
  8. 2021StyleFusion: A Generative Model for Disentangling Spatial Segmentscited by 28
  9. 2021Where and What? Examining Interpretable Disentangled Representationscited by 27
  10. 2021Commutative Lie Group VAE for Disentanglement Learningcited by 27
  11. 2023MEGANE: Morphable Eyeglass and Avatar Networkcited by 24
  12. 2023Face Aging via Diffusion-based Editingcited by 24
  13. 2023Boundary Guided Learning-Free Semantic Control with Diffusion Modelscited by 23
  14. 2022Custom Structure Preservation in Face Agingcited by 19
  15. 2022On the Versatile Uses of Partial Distance Correlation in Deep Learningcited by 18
  16. 2022Attri-VAE: attribute-based interpretable representations of medical images with variational autoencoderscited by 17
  17. 2022GAN-based Facial Attribute Manipulationcited by 16
  18. 2021Scaling-up Disentanglement for Image Translationcited by 15
  19. 2021Retrieve in Style: Unsupervised Facial Feature Transfer and Retrievalcited by 15
  20. 2023MMNet: Multi-Collaboration and Multi-Supervision Network for Sequential Deepfake Detectioncited by 15
  21. 2021DeepBlur: A Simple and Effective Method for Natural Image Obfuscationcited by 13
  22. 2024Uncovering the Text Embedding in Text-to-Image Diffusion Modelscited by 13
  23. 2021Style Equalization: Unsupervised Learning of Controllable Generative Sequence Modelscited by 11
  24. 2022Disentangling representations in Restricted Boltzmann Machines without adversariescited by 11
  25. 2022Fair NLP Models with Differentially Private Text Encoderscited by 9
  26. 2022GANStrument: Adversarial Instrument Sound Synthesis with Pitch-invariant Instance Conditioningcited by 9
  27. 2024AstroPT: Scaling Large Observation Models for Astronomycited by 9
  28. 2022IA-FaceS: A Bidirectional Method for Semantic Face Editingcited by 8
  29. 2022CLIP2GAN: Towards Bridging Text with the Latent Space of GANscited by 8
  30. 2023Exploring XAI for the Arts: Explaining Latent Space in Generative Musiccited by 8
  31. 2023Face Identity-Aware Disentanglement in StyleGANcited by 8
  32. 2021Unsupervised Disentanglement of Linear-Encoded Facial Semanticscited by 7
  33. 2021Disentanglement Learning for Variational Autoencoders Applied to Audio-Visual Speech Enhancementcited by 7
  34. 2021Is Disentanglement enough? On Latent Representations for Controllable Music Generationcited by 7
  35. 2022Zero-Shot Style Transfer for Gesture Animation driven by Text and Speech using Adversarial Disentanglement of Multimodal Style Encodingcited by 7
  36. 2024Double InfoGAN for Contrastive Analysiscited by 7
  37. 2022Towards Unifying the Label Space for Aspect- and Sentence-based Sentiment Analysiscited by 6
  38. 2022Disentangling speech from surroundings with neural embeddingscited by 6

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