Matches in Nanopublications for { ?s ?p <http://www.wikidata.org/entity/Q192776> ?g. }
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- Attention%20mechanisms%20in%20the%20U-Net%20architecture%20enabled%20the%20model%20to%20focus%20on%20burnt%20areas%20and%20improve%20accuracy%20and%20efficiency%2C%20especially%20in%20detecting%20edges%20and%20small%20areas%20where%20burnt%20and%20non-burnt%20pixels%20are%20mixed%20together. about Q192776 assertion.
- The%20model%20training%20utilized%20a%20batch%20size%20of%206%2C%20conducted%2050%20epochs%2C%20initialized%20the%20learning%20rate%20at%200.1%20with%20scheduled%20decrease%20by%20a%20factor%20of%200.5%20between%20the%2030th%20to%2050th%20epochs%2C%20and%20used%20Stochastic%20Gradient%20Descent%20optimizer. about Q192776 assertion.
- BiAU-Net%20outperformed%20traditional%20machine%20learning%20models%20including%20Support%20Vector%20Machines%20and%20Random%20Forest%2C%20which%20require%20handcrafted%20engineering%20and%20often%20perform%20well%20in%20training%20areas%20but%20poorly%20when%20transferred%20to%20different%20areas%20due%20to%20varying%20environments. about Q192776 assertion.
- Focal%20Loss%20addresses%20class%20imbalance%20by%20down-weighting%20easy-to-classify%20examples%20and%20placing%20more%20emphasis%20on%20hard-to-classify%20examples%20through%20tunable%20parameters%20%CE%B1%20%28weight%20between%20positive%20and%20negative%20samples%29%20and%20%CE%B3%20%28focusing%20parameter%20for%20adjusting%20loss%20magnitude%29. about Q192776 assertion.
- The%20BiAU-Net%20architecture%20incorporates%20attention%20gates%20at%20each%20up-sampling%20step%20that%20dynamically%20assess%20feature%20relevance%20to%20create%20attention%20weights%2C%20amplifying%20significant%20features%20and%20reducing%20the%20influence%20of%20unnecessary%20ones. about Q192776 assertion.
- The%20model%20processes%20pre-fire%20and%20post-fire%20satellite%20images%20through%20two%20symmetrical%20convolutions%20and%20max-pooling%20in%20the%20encoder%20phase%20to%20extract%20features%2C%20with%20four%20rounds%20of%20down-sampling%20producing%20four%20pairs%20of%20pre-%20and%20post-fire%20feature%20maps%20across%20varying%20scales. about Q192776 assertion.