Tackling Class Imbalance
A recent workshop focused on the challenges of class imbalance in computer vision, particularly when dealing with limited and biased data. Key discussions highlighted the need for innovative approaches, such as novelty detection and autoencoders, to identify defects in industrial inspection scenarios where data on damaged parts is scarce. These techniques aim to learn normal visual patterns and effectively flag anomalies, offering powerful solutions across various applications.In this clip
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The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Simulation and Synthetic Data for Computer Vision with Batu Arisoy - TWiML Talk #281
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