LinSlot: Exploiting Linear Representation hypothesis for unsupervised attribute discovery from slot based object representation

arXiv:2610.10722 · cs.CV, cs.AI · Submitted 2026-10-07 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-10-07

Updated: 2026-10-07

Terminology

• Generative Model — 11× in this paper · explained in 2 episodes
• Linear Representation Hypothesis — 8× in this paper
• Representation Learning — 7× in this paper · explained in 2 episodes
• Cosine Similarity — 6× in this paper · explained in 3 episodes
• Transformer — 6× in this paper · explained in 2 episodes
• International Conference on Machine Learning — 5× in this paper
• Benchmark — 4× in this paper · explained in 2 episodes
• Generative AI — 4× in this paper · explained in 2 episodes
• Clustering — 3× in this paper · explained in 2 episodes
• Diffusion Model — 3× in this paper · explained in 8 episodes
• Diffusion Models — 3× in this paper · explained in 6 episodes
• Latent Representation — 3× in this paper · explained in 2 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Steering — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Attention Mechanism — 2× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Core — 2× in this paper · explained in 2 episodes
• Decode — 2× in this paper · explained in 2 episodes
• Generative Modeling — 2× in this paper · explained in 3 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Iterative Refinement — 2× in this paper · explained in 3 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Sample Efficiency — 2× in this paper · explained in 4 episodes
• Compositional Generalization — 1× in this paper · explained in 4 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Gaussian Mixture Model (GMM) — 1× in this paper · explained in 2 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• MONet: Unsupervised Scene Decomposition and Representation
• Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders
• Structuring Sparsity: Block-Sparse Featurizers Capture Visual Concept Manifolds
• Multi-Object Representation Learning with Iterative Variational Inference
• On the Binding Problem in Artificial Neural Networks
• ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation
• Improved Object-Centric Diffusion Learning with Registers and Contrastive Alignment
• R-SQAIR: Relational Sequential Attend, Infer, Repeat
• Visual Concepts Tokenization

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