mlsanityData to 7 Oct 2026

Sparse Modeling for Image and Vision Processing

arXiv 1411.3230 ↗First posted 12 Nov 2014cs.CVcited by 48

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Abstract (arXiv)

In recent years, a large amount of multi-disciplinary research has been conducted on sparse models and their applications. In statistics and machine learning, the sparsity principle is used to perform model selection---that is, automatically selecting a simple model among a large collection of them. In signal processing, sparse coding consists of representing data with linear combinations of a few dictionary elements. Subsequently, the corresponding tools have been widely adopted by several scientific communities such as neuroscience, bioinformatics, or computer vision. The goal of this monograph is to offer a self-contained view of sparse modeling for visual recognition and image processing. More specifically, we focus on applications where the dictionary is learned and adapted to data, yielding a compact representation that has been successful in various contexts.

Key facts. Cited by 48 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Nov 2014.

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Citing papers per year: 2021: 8; 2022: 7; 2023: 10; 2024: 8; 2025: 7; 2026: 8.

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  1. 2024Scaling and evaluating sparse autoencoderscited by 530
  2. 2022The Lazy Neuron Phenomenon: On Emergence of Activation Sparsity in Transformerscited by 121
  3. 2021Toward Understanding the Feature Learning Process of Self-supervised Contrastive Learningcited by 119
  4. 2023A Holistic Approach to Unifying Automatic Concept Extraction and Concept Importance Estimationcited by 108
  5. 2025Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Modelscited by 67
  6. 2025Universal Sparse Autoencoders: Interpretable Cross-Model Concept Alignmentcited by 62
  7. 2021A Survey on Ensemble Learning under the Era of Deep Learningcited by 47
  8. 2021A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restorationcited by 34
  9. 2025Interpreting the linear structure of vision-language model embedding spacescited by 32
  10. 2023Which Features are Learnt by Contrastive Learning? On the Role of Simplicity Bias in Class Collapse and Feature Suppressioncited by 30
  11. 2024Decoding Dark Matter: Specialized Sparse Autoencoders for Interpreting Rare Concepts in Foundation Modelscited by 15
  12. 2022Revisiting Sparse Convolutional Model for Visual Recognitioncited by 13
36 more
  1. 2022Entropic Descent Archetypal Analysis for Blind Hyperspectral Unmixingcited by 11
  2. 2023Learning Sparse and Low-Rank Priors for Image Recovery via Iterative Reweighted Least Squares Minimizationcited by 10
  3. 2024Arbitrary-Scale Video Super-Resolution with Structural and Textural Priorscited by 9
  4. 2024Adaptive Sparse Allocation with Mutual Choice & Feature Choice Sparse Autoencoderscited by 9
  5. 2025Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictionscited by 9
  6. 2026Cross-Modal Redundancy and the Geometry of Vision-Language Embeddingscited by 9
  7. 2022Can Querying for Bias Leak Protected Attributes? Achieving Privacy With Smooth Sensitivitycited by 8
  8. 2022Cross-Modality Neuroimage Synthesis: A Surveycited by 6
  9. 2022Optimal Regularization for a Data Sourcecited by 6
  10. 2022Reverse Engineering ℓp attacks: A block-sparse optimization approach with recovery guaranteescited by 4
  11. 2023Closed-Loop Transcription via Convolutional Sparse Codingcited by 4
  12. 2021Sparse Coding with Multi-Layer Decoders using Variance Regularizationcited by 3
  13. 2024The Star Geometry of Critic-Based Regularizer Learningcited by 3
  14. 2026Structuring Sparsity: Block-Sparse Featurizers Capture Visual Concept Manifoldscited by 3
  15. 2025Interpretable 3D Neural Object Volumes for Robust Conceptual Reasoningcited by 2
  16. 2023Recovering Simultaneously Structured Data via Non-Convex Iteratively Reweighted Least Squarescited by 1
  17. 2023Sparse Models for Machine Learningcited by 1
  18. 2023Fixing the NTK: From Neural Network Linearizations to Exact Convex Programscited by 1
  19. 2025Evaluating Sparse Autoencoders: From Shallow Design to Matching Pursuitcited by 1
  20. 2026SCoCCA: Multi-modal Sparse Concept Decomposition via Canonical Correlation Analysiscited by 1
  21. 2021Metalearning: Sparse Variable-Structure Automatacited by 0
  22. 2021Improved Image Generation via Sparse Modelingcited by 0
  23. 2021Screening for a Reweighted Penalized Conditional Gradient Methodcited by 0
  24. 2021Cascaded Compressed Sensing Networks: A Reversible Architecture for Layerwise Learningcited by 0
  25. 2023Structured Learning in Time-dependent Cox Modelscited by 0
  26. 2023Semi-sparsity Priors for Image Structure Analysis and Extractioncited by 0
  27. 2023Learning Sparse Codes with Entropy-Based ELBOscited by 0
  28. 2024Seismic Traveltime Tomography with Label-free Learningcited by 0
  29. 2024IB-AdCSCNet:Adaptive Convolutional Sparse Coding Network Driven by Information Bottleneckcited by 0
  30. 2024Few-Shot Airway-Tree Modeling using Data-Driven Sparse Priorscited by 0
  31. 2025BasicAVSR: Arbitrary-Scale Video Super-Resolution via Image Priors and Enhanced Motion Compensationcited by 0
  32. 2026Supervised sparse auto-encoders for interpretable and compositional representationscited by 0
  33. 2026Extending Occam's inversion with lasso fusion, overcomplete dictionaries, and isotropic total variation regularisationcited by 0
  34. 2026From Interpretability Methods to Interpretable Modelscited by 0
  35. 2026MARCEDES: Score-based causal discovery under non-Gaussianity with continuous optimizationcited by 0
  36. 2026Interference Beyond Geometry in Concept Extractioncited by 0