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The 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real): Methods and Results

arXiv 2509.10463 ↗First posted 15 Aug 2025cs.LG cs.CVcited by 0

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Abstract (arXiv)

This paper reviews the 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real), held in conjunction with ICCV 2025. The workshop aimed to bridge the gap between the theoretical promise of Disentangled Representation Learning (DRL) and its application in realistic scenarios, moving beyond synthetic benchmarks. DRL4Real focused on evaluating DRL methods in practical applications such as controllable generation, exploring advancements in model robustness, interpretability, and generalization. The workshop accepted 9 papers covering a broad range of topics, including the integration of novel inductive biases (e.g., language), the application of diffusion models to DRL, 3D-aware disentanglement, and the expansion of DRL into specialized domains like autonomous driving and EEG analysis. This summary details the workshop's objectives, the themes of the accepted papers, and provides an overview of the methodologies proposed by the authors.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 20 references that are in the map. First posted on arXiv 15 Aug 2025.

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References · 15

  1. 2024Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detectioncited by 30
  2. 2024Diffusion Model with Cross Attention as an Inductive Bias for Disentanglementcited by 27
  3. 2024Rate-Distortion-Cognition Controllable Versatile Neural Image Compressioncited by 11
  4. 2025Taming Anomalies with Down-Up Sampling Networks: Group Center Preserving Reconstruction for 3D Anomaly Detectioncited by 11
  5. 2024Graph-based Unsupervised Disentangled Representation Learning via Multimodal Large Language Modelscited by 10
  6. 2024Closed-Loop Unsupervised Representation Disentanglement with β-VAE Distillation and Diffusion Probabilistic Feedbackcited by 9
  7. 2025Spatial Distillation based Distribution Alignment (SDDA) for Cross-Headset EEG Classificationcited by 7
  8. 2025MIRepNet: A Pipeline and Foundation Model for EEG-Based Motor Imagery Classificationcited by 5
  9. 2024Imagining the Unseen: Generative Location Modeling for Object Placementcited by 4
  10. 2025Fence Theorem: Towards Dual-Objective Semantic-Structure Isolation in Preprocessing Phase for 3D Anomaly Detectioncited by 4
  11. 2025CLEAN-MI: A Scalable and Efficient Pipeline for Constructing High-Quality Neurodata in Motor Imagery Paradigmcited by 3
  12. 2024UMMAN: Unsupervised Multi-graph Merge Adversarial Network for Disease Prediction Based on Intestinal Floracited by 2
3 more
  1. 2024Unsupervised Representation Learning from Sparse Transformation Analysiscited by 2
  2. 2025Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fieldscited by 2
  3. 2025DiViD: Disentangled Video Diffusion for Static-Dynamic Factorizationcited by 1

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  1. 2024Disentangling Disentangled Representations: Towards Improved Latent Units via Diffusion Models
  2. 2024DEAR: Disentangled Environment and Agent Representations for Reinforcement Learning without Reconstruction
  3. 2024Disentangling Representations through Multi-task Learning
  4. 2026Self-Supervised Consistency Enhanced Disentangled Learning for Neural Decoding Generalization in Brain-Machine Interface
  5. 2023DisDiff: Unsupervised Disentanglement of Diffusion Probabilistic Models
  6. 2026dVLA-RL: Reinforcement Learning over Denoising Trajectories for Discrete Diffusion Vision-Language-Action Models
  7. 2026D3-MoE:Dual Disentangled Diffusion Mixture-of-Experts for Style-Controllable End-to-End Autonomous Driving
  8. 2024Revisiting Disentanglement in Downstream Tasks: A Study on Its Necessity for Abstract Visual Reasoning