{"schema_version":"1.0","canonical_url":"https://patentable.app/patents/US-10535141","patent":{"patent_number":"US-10535141","title":"Differentiable jaccard loss approximation for training an artificial neural network","assignee":null,"inventors":[],"filing_date":"2018-02-06T00:00:00.000Z","publication_date":"2020-01-14T00:00:00.000Z","cpc_codes":["G06T","G06N","G06N","G06N","G06N","G06T","G06T","G06T"],"num_claims":17,"abstract":"Systems and methods described herein may relate to training an artificial neural network (ANN) using a differentiable Jaccard Loss approximation. An example embodiment may involve obtaining a training image and a corresponding ground truth mask that represents a desired segmentation of the training image. The embodiment may further involve applying an ANN on the training image to generate an output segmentation of the training image that depends on a plurality of weights of the ANN and determining a differentiable Jaccard Loss approximation based on the output segmentation of the training image and the ground truth mask. The embodiment also involves modifying one or more weights of the ANN based on the differentiable Jaccard Loss approximation and providing a representation of the ANN as modified to a mobile computing device."},"analysis":{"summary":null,"layman_explanation":null,"technical_analysis":null,"business_analysis":null,"faqs":null,"topics":[],"tech_cluster":null},"seo":{"title":"Differentiable jaccard loss approximation for training an artificial neural network","description":"Systems and methods described herein may relate to training an artificial neural network (ANN) using a differentiable Jaccard Loss approximation. An example embodiment may involve obtaining a training","keywords":[]},"attribution":{"source":"Patentable","source_url":"https://patentable.app","canonical_url":"https://patentable.app/patents/US-10535141","license":"CC-BY-4.0-like","license_terms":"AI-generated analysis on this page (summary, layman_explanation, technical_analysis, business_analysis, faqs) may be reused with attribution and a visible link back to the canonical URL above. Patent abstracts, claims, and bibliographic data are USPTO public domain.","required_link":"https://patentable.app/patents/US-10535141","citation_suggestion":"Patentable. \"Differentiable jaccard loss approximation for training an artificial neural network\" (US-10535141). https://patentable.app/patents/US-10535141","copyright_holder":"Nomic Interactive Technology LLC"},"links":{"html":"https://patentable.app/patents/US-10535141","json":"https://patentable.app/api/llm-context/US-10535141","site":"https://patentable.app","llms_txt":"https://patentable.app/llms.txt"},"generated_at":"2026-05-31T05:37:54.094Z"}