We lifted 3 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| intelligolabs/diag | canonical | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| evaluate | Ran | intelligolabs/diag/train_ResNet50.py pointer only (licence: NONE) · get_code("8ea47978cfb7cc95") |
| get_negative_images | Ran | intelligolabs/diag/generate_augmented_images.py pointer only (licence: NONE) · get_code("1cd1f6fc49321cc5") |
| get_positive_images_maks | Ran | intelligolabs/diag/generate_augmented_images.py pointer only (licence: NONE) · get_code("5b106cd595347a77") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Defect detection is the task of identifying defects in production samples. Usually, defect detection classifiers are trained on ground-truth data formed by normal samples (negative data) and samples with defects (positive data), where the latter are consistently fewer than normal samples. State-of-the-art data augmentation procedures add synthetic defect data by superimposing artifacts to normal samples to mitigate problems related to unbalanced training data. These techniques often produce out-of-distribution images, resulting in systems that learn what is not a normal sample but cannot accurately identify what a defect looks like. In this work, we introduce DIAG, a training-free Diffusion-based In-distribution Anomaly Generation pipeline for data augmentation. Unlike conventional image generation techniques, we implement a human-in-the-loop pipeline, where domain experts provide multimodal guidance to the model through text descriptions and region localization of the possible anomalies. This strategic shift enhances the interpretability of results and fosters a more robust human feedback loop, facilitating iterative improvements of the generated outputs. Remarkably, our approach operates in a zero-shot manner, avoiding time-consuming fine-tuning procedures while achieving superior performance. We demonstrate the efficacy and versatility of DIAG with respect to state-of-the-art data augmentation approaches on the challenging KSDD2 dataset, with an improvement in AP of approximately 18% when positive samples are available and 28% when they are missing. The source code is available at https://github.com/intelligolabs/DIAG.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2407.03961")
get_code_for_paper("2407.03961")
have("2407.03961")
Connect an agent — have() is free.