[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-multiplicative-residual-module-lifts-vision-nets-performance":10},{"siteName":4,"siteTagline":5,"publisherName":4,"contactEmail":6},"The Revision","Tech news, decoded.","editor@therevision.news",{"gaMeasurementId":8,"adsenseClientId":9},"G-ZW2MV82GYR","ca-pub-8533917693782264",{"article":11},{"id":12,"slug":13,"title":14,"dek":15,"body_md":16,"tags_json":17,"published_at":18,"created_at":19,"updated_at":20,"status":21,"review_note":22,"review_notes":23,"image_url":22,"persona_id":22,"persona_name":22,"section":22,"tags":24,"sources":27,"feedback":31,"feedback_at":22,"cost_usd":31,"total_tokens":31},1309,"multiplicative-residual-module-lifts-vision-nets-performance","Multiplicative residual module lifts vision nets performance","PURe replaces standard residual units with a log‑domain product unit, giving image classifiers and CT segmenters higher accuracy for fewer parameters.","- New module lets existing CNNs use multiplicative interactions.\n\nPURe introduces a 2‑D product unit that works in the log domain, sidestepping the instability that has kept multiplicative layers out of deep nets. The authors drop it into ResNet‑style blocks and test on Galaxy10 DECaLS, ImageNet and CIFAR‑10, as well as slice‑based CT segmentation on the AMOS benchmark. Across the board the modified networks either match deeper ResNets with half the parameters or exceed them outright.\n\nThe significance lies in proving that explicit multiplicative aggregation can be a practical design primitive. It narrows the accuracy‑parameter gap, which could matter for edge devices and medical imaging pipelines that cannot afford large models.\n\nSo far the gains are modest but consistent, suggesting a new lever for architects tired of squeezing performance from additive layers alone.","[\"computer-vision\",\"deep-learning\"]","2026-06-16T04:00:00.000Z","2026-06-17T03:07:09.531Z","2026-06-17T03:07:12.348Z","published",null,[],[25,26],"computer-vision","deep-learning",[28],{"name":29,"url":30},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2505.04397",0]