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Deep4ge dataset released for DNN fault detection and diagnosis · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced Deep4ge, a new benchmark dataset designed to aid in the detection and diagnosis of faults within deep learning systems. The dataset comprises over 14,000 training runs generated from 59 adapted TensorFlow/Keras programs sourced from Stack Overflow. These runs include nearly 10,000 faulty variants created through 27 source-code transformations, alongside over 4,000 correct baseline runs. Deep4ge captures 26 features per epoch, such as weights, gradients, and accuracy trends, to support tasks like binary fault detection, multi-class diagnosis, and early prediction. AI

IMPACT Provides a standardized dataset for improving the reliability and robustness of deep learning models.

RANK_REASON The cluster describes a new dataset and framework for research purposes, released on arXiv.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Deep4ge dataset released for DNN fault detection and diagnosis · 2 sources tracked

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Sigma Jahan ·

    Deep4ge: DNN Training Trajectories for Fault Detection and Diagnosis

    arXiv:2607.12868v1 Announce Type: cross Abstract: Deep learning systems often fail due to subtle implementation faults that alter training behavior. Recent work has studied how to detect and diagnose such failures from changes observed across training epochs. However, the softwar…

  2. arXiv cs.LG TIER_1 English(EN) · Sigma Jahan ·

    Deep4ge: DNN Training Trajectories for Fault Detection and Diagnosis

    Deep learning systems often fail due to subtle implementation faults that alter training behavior. Recent work has studied how to detect and diagnose such failures from changes observed across training epochs. However, the software engineering community still lacks a public datas…

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