DUA-D2C: Dynamic Uncertainty Aware Method for Overfitting Remediation in Deep Learning

arXiv:2411.15876 · cs.LG, cs.AI, cs.CV, cs.NE, stat.ML · Submitted 2024-11-24 · Read on arXiv

cs.LG, cs.AI, cs.CV, cs.NE, stat.ML

Submitted: 2024-11-24

Updated: 2026-09-05

Comments: This version (v2) extends our previous work (arXiv:2411.15876v1) on Divide2Conquer (D2C) by introducing Dynamic Uncertainty-Aware Divide2Conquer (DUA-D2C). The manuscript has been published at Complex and Intelligent Systems. Find the published version at https://doi.org/10.1007/s40747-026-02251-1

Journal ref: Complex Intell. Syst. 12, 149 (2026)

DOI: 10.1007/s40747-026-02251-1

Code: https://github.com/Saiful185/DUAD2C

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 85× in this paper · explained in 1 episode
• Generalization — 43× in this paper · explained in 13 episodes
• Aggregation — 42× in this paper · explained in 1 episode
• AUC — 22× in this paper · explained in 2 episodes
• Regularization — 14× in this paper · explained in 3 episodes
• Benchmark — 7× in this paper · explained in 2 episodes
• F1 Score — 7× in this paper · explained in 3 episodes
• Data Augmentation — 6× in this paper · explained in 7 episodes
• Dynamic Weighting — 3× in this paper · explained in 3 episodes
• Reinforcement Learning — 3× in this paper · explained in 4 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Core — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Federated Learning — 2× in this paper · explained in 14 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Ablation Studies — 1× in this paper · explained in 1 episode
• Abundance — 1× in this paper · explained in 1 episode
• Actor — 1× in this paper · explained in 3 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Affective Computing — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Bayesian Inference — 1× in this paper · explained in 5 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 1× in this paper
• Covariate Shift — 1× in this paper · explained in 2 episodes
• Feature Engineering — 1× in this paper · explained in 3 episodes
• Institute for Advanced Research — 1× in this paper
• Mean Squared Error — 1× in this paper
• Non-IID Data — 1× in this paper · explained in 2 episodes
• Pruning — 1× in this paper · explained in 4 episodes

Sources

• The Theory Behind Overfitting, Cross Validation, Regularization, Bagging, and Boosting: Tutorial
• Federated Optimization:Distributed Optimization Beyond the Datacenter
• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

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