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cs.CV2025
AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training
Feiyang Kang, Nadine Chang, Maying Shen +4
The computational burden and inherent redundancy of large-scale datasets challenge the training of contemporary machine learning models. Data pruning offers a solution by selecting…
cs.LG2025
Minority Reports: Balancing Cost and Quality in Ground Truth Data Annotation
Hsuan Wei Liao, Christopher Klugmann, Daniel Kondermann +1
High-quality data annotation is an essential but laborious and costly aspect of developing machine learning-based software. We explore the inherent tradeoff between annotation accu…