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Improving generalization with synthetic training data for deep learning based quality inspection
As a consequence, models trained under such constraints are expected to be very sensitive to input distribution changes, which may be caused in practice by changes in the acquisition system (cameras, lights), in the parts or in the defects aspect.
Data refinement for fully unsupervised visual inspection using pre-trained networks
In this work, we first assess the robustness of these pre-trained methods to fully unsupervised context, using polluted training sets (i. e. containing defective samples), and show that these methods are more robust to pollution compared to methods such as CutPaste.
Data augmentation and pre-trained networks for extremely low data regimes unsupervised visual inspection
The use of deep features coming from pre-trained neural networks for unsupervised anomaly detection purposes has recently gathered momentum in the computer vision field.
Synthetic training data generation for deep learning based quality inspection
In order to detect defects, supervised learning is often utilized, but necessitates a large amount of annotated images, which can be costly: collecting, cleaning, and annotating the data is tedious and limits the speed at which a system can be deployed as everything the system must detect needs to be observed first.
Active learning using weakly supervised signals for quality inspection
In this work, we develop a methodology for learning actively, from rapidly mined, weakly (i. e. partially) annotated data, enabling a fast, direct feedback from the operators on the production line and tackling a big machine vision weakness: false positives.
