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Status: Unchecked ×Keyword: training data selection ×Clear all

4 claims from 2 papers

1. Computer Science › Advanced Neural Network Applications

   Bridging the Gap Between Anchor-Based and Anchor-Free Detection via Adaptive Training Sample Selection

   Zhang, Chi, Yao, Lei and Li · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 2020

   The paper argues that how training samples are labelled positive or negative explains the gap between anchor-based and anchor-free detectors, and proposes ATSS, an adaptive selection method, to improve both.

   Unchecked2 claims
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   1. UncheckedWhen anchor-based and anchor-free detectors define positive and negative training samples the same way, their final performance shows no obvious difference.“If they adopt the same definition of positive and negative samples during training, there is no obvious difference in the final performance, no matter regressing from a box or a point.”
   2. Unchecked“With the newly introduced ATSS, we improve state-of-the-art detectors by a large margin to $50.7\%$ AP without introducing any overhead.”

2. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Using Machine Learning to Parameterize Moist Convection: Potential for Modeling of Climate, Climate Change, and Extreme Events

   O'Gorman and Dwyer · DSpace@MIT (Massachusetts Institute of Technology) · 2018

   Unchecked2 claims
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   1. Unchecked“The GCM with the ML convective parameterization runs stably and accurately captures important climate statistics including precipitation extremes without the need for special training on extremes.”
   2. Unchecked“Remarkably, climate change is also captured when training only on the warm climate, and this is because the extratropics of the warm climate provides training samples for the tropics of the control climate.”