mlsanityData to 7 Oct 2026

Generalization Error in Deep Learning

arXiv 1808.01174 ↗First posted 3 Aug 2018cs.LG cs.AI stat.MLcited by 46

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Abstract (arXiv)

Deep learning models have lately shown great performance in various fields such as computer vision, speech recognition, speech translation, and natural language processing. However, alongside their state-of-the-art performance, it is still generally unclear what is the source of their generalization ability. Thus, an important question is what makes deep neural networks able to generalize well from the training set to new data. In this article, we provide an overview of the existing theory and bounds for the characterization of the generalization error of deep neural networks, combining both classical and more recent theoretical and empirical results.

Key facts. Cited by 46 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Aug 2018.

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Citing papers per year: 2021: 8; 2022: 9; 2023: 12; 2024: 9; 2025: 4; 2026: 4.

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  2. 2023On the Stability of Iterative Retraining of Generative Models on their own Datacited by 98
  3. 2024Self-Correcting Self-Consuming Loops for Generative Model Trainingcited by 34
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  7. 2022On Rademacher Complexity-based Generalization Bounds for Deep Learningcited by 16
  8. 2022Bounding the Rademacher Complexity of Fourier neural operatorscited by 16
  9. 2023Unsupervised Layer-wise Score Aggregation for Textual OOD Detectioncited by 15
  10. 2024Deeper or Wider: A Perspective from Optimal Generalization Error with Sobolev Losscited by 14
  11. 2022Pyramid-BERT: Reducing Complexity via Successive Core-set based Token Selectioncited by 13
  12. 2022Topology and geometry of data manifold in deep learningcited by 12
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  4. 2024Information-Theoretic Generalization Bounds for Deep Neural Networkscited by 10
  5. 2021Generalization Error Bounds on Deep Learning with Markov Datasetscited by 9
  6. 2024Universal Novelty Detection Through Adaptive Contrastive Learningcited by 9
  7. 2024Generalization Error Bound for Quantum Machine Learning in NISQ Era -- A Surveycited by 7
  8. 2023Generalization and Estimation Error Bounds for Model-based Neural Networkscited by 6
  9. 2023Deep neural networks architectures from the perspective of manifold learningcited by 5
  10. 2022Combining Varied Learners for Binary Classification using Stacked Generalizationcited by 4
  11. 2023Global Convergence Rate of Deep Equilibrium Models with General Activationscited by 4
  12. 2023Optimization Guarantees of Unfolded ISTA and ADMM Networks With Smooth Soft-Thresholdingcited by 3
  13. 2021HierMUD: Hierarchical Multi-task Unsupervised Domain Adaptation between Bridges for Drive-by Damage Diagnosiscited by 2
  14. 2021Error Bounds for a Matrix-Vector Product Approximation with Deep ReLU Neural Networkscited by 2
  15. 2023Lipschitzness Effect of a Loss Function on Generalization Performance of Deep Neural Networks Trained by Adam and AdamW Optimizerscited by 2
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  18. 2024Bias of Stochastic Gradient Descent or the Architecture: Disentangling the Effects of Overparameterization of Neural Networkscited by 2
  19. 2025Runtime Safety Monitoring of Deep Neural Networks for Perception: A Surveycited by 2
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  21. 2023Stretched and measured neural predictions of complex network dynamicscited by 1
  22. 2024Data-Dependent Generalization Bounds for Parameterized Quantum Models Under Noisecited by 1
  23. 2025MEL: Multi-level Ensemble Learning for Resource-Constrained Environmentscited by 1
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  27. 2021Understanding Adversarial Examples Through Deep Neural Network's Response Surface and Uncertainty Regionscited by 0
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  31. 2025Path Regularization: A Near-Complete and Optimal Nonasymptotic Generalization Theory for Multilayer Neural Networks and Double Descent Phenomenoncited by 0
  32. 2026Aligning Network Equivariance with Data Symmetry: A Theoretical Framework and Adaptive Approach for Image Restorationcited by 0
  33. 2026Mixture-of-Experts Diffusion Models for Adaptive Massive MIMO Channel Estimation via Variational Bayesian Inferencecited by 0
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