![]() ![]() Into multiple lightweight derived models. An orthogonal contribution tackles theĬoncurrent adaptation and computational acceleration of a large source model Several widely used network architectures into CINs, including 3D CNNs, The benefit to online inference is demonstrated by reformulating These advances are attained through aīottom-up computational reorganization and judicious architectural State-of-the-art methods developed for offline processing of spatio-temporalĭata and reuse their pre-trained weights, improving their online processingĮfficiency by an order of magnitude. Proposed and explored across four publications. Here, the concept of Continual Inference Networks (CINs) is Specifically, a core contribution addresses the efficiency aspects during Instead of pursuing yet another increase in predictive performance, thisĭissertation is dedicated to the improvement of neural network efficiency. To capitalize on this correlation for a more efficient solution to video processing, we previously presented a method to linearize part of a DNN or an entire. The economic cost and negativeĮnvironmental externalities of training and serving models is in evidentĭisharmony with financial viability and climate action goals. Their uses are exhilarating, the continually increasing model sizes andĬomputational complexities have a dark side. Prose, becoming the topic of everyday dinner-table conversations. Large language models answer wide-ranging questions, generate code, and write Download a PDF of the paper titled Efficient Online Processing with Deep Neural Networks, by Lukas Hedegaard Download PDF Abstract: The capabilities and adoption of deep neural networks (DNNs) grow at anĮxhilarating pace: Vision models accurately classify human actions in videosĪnd identify cancerous tissue in medical scans as precisely than human experts ![]()
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