DELOS applies contrastive learning to phase-folded light curves to detect shallow intermediate-to-long period transits, reporting 15.5% and 11.25% gains in combined precision-recall over BLS and TLS in low-SNR tests plus 3-80x speedups.
A simple framework for contrastive learning of visual representations, in: Pro- ceedings of the 37th International Conference on Machine Learning, JMLR.org
3 Pith papers cite this work. Polarity classification is still indexing.
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RBFN projection heads serve as competitive replacements for MLP heads in SSL and enable SNS, a label-free metric from RBF parameters that correlates strongly with logistic regression evaluation.
MeCSAFNet reports mIoU gains of 4.8-19.6% over U-Net and SegFormer baselines on FBP and Potsdam datasets by processing spectral channels separately and fusing features with CBAM attention.
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Radial Basis Function Networks as Projection Heads in Self-Supervised Learning
RBFN projection heads serve as competitive replacements for MLP heads in SSL and enable SNS, a label-free metric from RBF parameters that correlates strongly with logistic regression evaluation.
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Multi-encoder ConvNeXt Network with Smooth Attentional Feature Fusion for Multispectral Semantic Segmentation
MeCSAFNet reports mIoU gains of 4.8-19.6% over U-Net and SegFormer baselines on FBP and Potsdam datasets by processing spectral channels separately and fusing features with CBAM attention.