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How Low Can You Go? Surfacing Prototypical In-Distribution Samples for Unsupervised Anomaly Detection
Additionally, we show that the prototypical in-distribution samples identified by our proposed methods translate well to different models and other datasets and that using their characteristics as guidance allows for successful manual selection of small subsets of high-performing samples.
Accuracy is not the only Metric that matters: Estimating the Energy Consumption of Deep Learning Models
Modern machine learning models have started to consume incredible amounts of energy, thus incurring large carbon footprints (Strubell et al., 2019).
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