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

Longwei Wang; Xueqian Li; Zheng Zhang

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

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2412.07022 v1 pith:MQZVX3O6 submitted 2024-12-09 cs.CV cs.AI

Longwei Wang , Xueqian Li , Zheng Zhang This is my paper · ORCID

classification cs.CVcs.AI

keywords ensembleconvolutionaldensedensenetneuralarchitecturecross-connecteddcc-ecnn

verification ladder T0 review T1 audit T2 compute T3 formal

0 comments

read the original abstract

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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI
cs.CV 2026-08 conditional novelty 6.0 of 10

A systematic audit of 175 papers finds that most uses of Grad-CAM on vision transformers omit the implementation choices needed to reproduce the visual explanation, and introduces a taxonomy to name those choices.
Mechanistic Interpretability of LLM Jailbreaks via Internal Attribution Graphs
cs.CR 2026-07 conditional novelty 6.0 of 10

On Llama-2-7B, path-rerouting magnitude in paired transcoder attribution graphs correlates with jailbreak success (r=0.461), while static node metrics and top-feature ablations do not.
Explainable Novel Category Discovery in Semantic Concept Space
cs.CV 2026-07 conditional novelty 6.0 of 10

xNCD routes novel category discovery through a CLIP-aligned concept bottleneck, matching strong NCD baselines while producing intrinsic cluster- and instance-level concept explanations.
Learning to Transmit: Volatility-Aware Predictive Communication for Energy-Efficient IoT Networks
cs.IT 2026-07 conditional novelty 5.0 of 10

Sensors using volatility-aware studentized residuals plus RLS online adaptation transmit up to 94.7% less IoT data while keeping reconstruction MAE at 0.35°C.

Pith tools