mlsanityData to 14 Oct 2026

Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness

arXiv 2412.07022 ↗First posted 9 Dec 2024cs.CV cs.AIcited by 9

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Abstract (arXiv)

The resilience of convolutional neural networks against input variations and adversarial attacks remains a significant challenge in image recognition tasks. Motivated by the need for more robust and reliable image recognition systems, we propose the Dense Cross-Connected Ensemble Convolutional Neural Network (DCC-ECNN). This novel architecture integrates the dense connectivity principle of DenseNet with the ensemble learning strategy, incorporating intermediate cross-connections between different DenseNet paths to facilitate extensive feature sharing and integration. The DCC-ECNN architecture leverages DenseNet's efficient parameter usage and depth while benefiting from the robustness of ensemble learning, ensuring a richer and more resilient feature representation.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 8 of its 21 references that are in the map. First posted on arXiv 9 Dec 2024.

Builds on

8 of 21 references.

References · 8

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  2. 2016Densely Connected Convolutional Networkscited by 6,720
  3. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,859
  4. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  5. 2019Benchmarking Neural Network Robustness to Common Corruptions and Perturbationscited by 2,877
  6. 2016Group Equivariant Convolutional Networkscited by 1,165
  7. 2014Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognitioncited by 720
  8. 2017Dual Path Networkscited by 119

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 5.

9 papers.

  1. 2024Bridging Interpretability and Robustness Using LIME-Guided Model Refinementcited by 8
  2. 2025Bridging Symmetry and Robustness: On the Role of Equivariance in Enhancing Adversarial Robustnesscited by 8
  3. 2025Promoting Shape Bias in CNNs: Frequency-Based and Contrastive Regularization for Corruption Robustnesscited by 4
  4. 2026Explainability-Guided Defense: Attribution-Aware Model Refinement Against Adversarial Data Attackscited by 3
  5. 2024Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learningcited by 2
  6. 2026Learning to Select Like Humans: Explainable Active Learning for Medical Imagingcited by 2
  7. 2026Explainable Novel Category Discovery in Semantic Concept Spacecited by 1
  8. 2026Mechanistic Interpretability of LLM Jailbreaks via Internal Attribution Graphscited by 0
  9. 2026Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AIcited by 0