CDNet generates contrastive training pairs via CNN-based diffusion transitions between time series samples and claims consistent accuracy gains for deep classifiers on binary UCR datasets.
Self-improvement of weighted pointwise inequalities on open sets
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abstract
We prove a general self-improvement property for a family of weighted pointwise inequalities on open sets, including pointwise Hardy inequalities with distance weights. For this purpose we introduce and study the classes of $p$-Poincar\'e and $p$-Hardy weights for an open set $\Omega\subset X$, where $X$ is a metric measure space. We also apply the self-improvement of weighted pointwise Hardy inequalities in connection with usual integral versions of Hardy inequalities.
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A Contrastive Diffusion-based Network (CDNet) for Time Series Classification
CDNet generates contrastive training pairs via CNN-based diffusion transitions between time series samples and claims consistent accuracy gains for deep classifiers on binary UCR datasets.