Deep4ge: DNN Training Trajectories for Fault Detection and Diagnosis

• URL: http://arxiv.org/abs/2607.12868v1
• Date: Tue, 14 Jul 2026 15:20:16 GMT
• Title: Deep4ge: DNN Training Trajectories for Fault Detection and Diagnosis
• Abstract summary: Deep4ge is a benchmark of 14,227 training runs generated from 59 adapted/Keras deep neural network (DNN) programs collected from Stack Overflow.<n>The dataset contains 9,845 faulty runs and 4,382 correct baseline runs.<n>Deep4ge supports binary fault detection, multi-class fault diagnosis, and early fault prediction from partial training runs.
• Score: 0.8883733362171032
• License: http://creativecommons.org/licenses/by/4.0/
• 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 software engineering community still lacks a public dataset of per-epoch training runs with documented fault history, feature extraction details, and clear reuse support for fault detection and diagnosis tasks. We present Deep4ge, a controlled benchmark of 14,227 training runs generated from 59 adapted TensorFlow/Keras deep neural network (DNN) programs collected from Stack Overflow. We generated faulty variants using 27 source-code transformations that introduce known faults across seven categories. The dataset contains 9,845 faulty runs and 4,382 correct baseline runs. For each run, we record 4 evaluation metrics and 26 features that measure training behavior at every epoch. These features capture weights, gradients, activations, accuracy and loss trends, learning rate, and hardware use. Deep4ge supports binary fault detection, multi-class fault diagnosis, and early fault prediction from partial training runs. We release the dataset and fault-injection framework at https://doi.org/10.5281/zenodo.20337241.

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories [11.597228102492672]
  We propose a model-agnostic method for detecting annotation errors in video datasets.<n>Our method does not require ground truth on annotation errors and is generalizable across datasets.<n>EgoPER and Cholec80 demonstrate strong detection performance, effectively identifying subtle inconsistencies such as mislabeling and frame disordering.
  arXiv  Detail & Related papers  (2026-02-16T19:53:58Z)
• Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification [3.2623791881739033]
  We present DEFault -- a novel technique to detect and diagnose faults in Deep Neural Networks (DNN) programs.<n>Our approach achieves 94% recall in detecting real-world faulty DNN programs and 63% recall in diagnosing the root causes of the faults, demonstrating 3.92% - 11.54% higher performance than that of state-of-the-art techniques.
  arXiv  Detail & Related papers  (2025-01-22T00:55:09Z)
• SINDER: Repairing the Singular Defects of DINOv2 [61.98878352956125]
  Vision Transformer models trained on large-scale datasets often exhibit artifacts in the patch token they extract. We propose a novel fine-tuning smooth regularization that rectifies structural deficiencies using only a small dataset.
  arXiv  Detail & Related papers  (2024-07-23T20:34:23Z)
• DF40: Toward Next-Generation Deepfake Detection [62.073997142001424]
  existing works identify top-notch detection algorithms and models by adhering to the common practice: training detectors on one specific dataset and testing them on other prevalent deepfake datasets. But can these stand-out "winners" be truly applied to tackle the myriad of realistic and diverse deepfakes lurking in the real world? We construct a highly diverse deepfake detection dataset called DF40, which comprises 40 distinct deepfake techniques.
  arXiv  Detail & Related papers  (2024-06-19T12:35:02Z)
• An Effective Data-Driven Approach for Localizing Deep Learning Faults [20.33411443073181]
  We propose a novel data-driven approach that leverages model features to learn problem patterns. Our methodology automatically links bug symptoms to their root causes, without the need for manually crafted mappings. Our results demonstrate that our technique can effectively detect and diagnose different bug types.
  arXiv  Detail & Related papers  (2023-07-18T03:28:39Z)
• Bridging Precision and Confidence: A Train-Time Loss for Calibrating Object Detection [58.789823426981044]
  We propose a novel auxiliary loss formulation that aims to align the class confidence of bounding boxes with the accurateness of predictions. Our results reveal that our train-time loss surpasses strong calibration baselines in reducing calibration error for both in and out-domain scenarios.
  arXiv  Detail & Related papers  (2023-03-25T08:56:21Z)
• PULL: Reactive Log Anomaly Detection Based On Iterative PU Learning [58.85063149619348]
  We propose PULL, an iterative log analysis method for reactive anomaly detection based on estimated failure time windows. Our evaluation shows that PULL consistently outperforms ten benchmark baselines across three different datasets.
  arXiv  Detail & Related papers  (2023-01-25T16:34:43Z)
• Self-Supervised Training with Autoencoders for Visual Anomaly Detection [61.62861063776813]
  We focus on a specific use case in anomaly detection where the distribution of normal samples is supported by a lower-dimensional manifold. We adapt a self-supervised learning regime that exploits discriminative information during training but focuses on the submanifold of normal examples. We achieve a new state-of-the-art result on the MVTec AD dataset -- a challenging benchmark for visual anomaly detection in the manufacturing domain.
  arXiv  Detail & Related papers  (2022-06-23T14:16:30Z)
• DeepFD: Automated Fault Diagnosis and Localization for Deep Learning Programs [15.081278640511998]
  DeepFD is a learning-based fault diagnosis and localization framework. It maps the fault localization task to a learning problem. It correctly diagnoses 52% faulty DL programs, compared with around half (27%) achieved by the best state-of-the-art works.
  arXiv  Detail & Related papers  (2022-05-04T08:15:56Z)
• Transfer Learning for Fault Diagnosis of Transmission Lines [55.971052290285485]
  A novel transfer learning framework based on a pre-trained LeNet-5 convolutional neural network is proposed. It is able to diagnose faults for different transmission line lengths and impedances by transferring the knowledge from a source neural network to predict a dissimilar target dataset.
  arXiv  Detail & Related papers  (2022-01-20T06:36:35Z)
• DeepDiagnosis: Automatically Diagnosing Faults and Recommending Actionable Fixes in Deep Learning Programs [12.917211542949786]
  We propose DeepDiagnosis, a novel approach that localizes the faults, reports error symptoms and suggests fixes for DNN programs. DeepDiagnosis manifests the best capabilities of fault detection, bug localization, and symptoms identification when compared to other approaches.
  arXiv  Detail & Related papers  (2021-12-07T23:15:23Z)
• TELESTO: A Graph Neural Network Model for Anomaly Classification in Cloud Services [77.454688257702]
  Machine learning (ML) and artificial intelligence (AI) are applied on IT system operation and maintenance. One direction aims at the recognition of re-occurring anomaly types to enable remediation automation. We propose a method that is invariant to dimensionality changes of given data.
  arXiv  Detail & Related papers  (2021-02-25T14:24:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.