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%252C+where+uniform+and+compact+features+are+learned.+Specifically%252C+we+generate+a+set+of+non-learnable+uniform+prototypes+before+each+task+starts.+Then+we+assign+these+uniform+prototypes+to+each+class+and+guide+the+feature+learning+through+prototype+contrastive+learning.+We+also+dynamically+adjust+the+relative+margin+between+old+and+new+classes+so+that+the+feature+distribution+will+be+maintained+balanced+and+compact.+Finally%252C+we+demonstrate+through+extensive+experiments+that+the+proposed+method+achieves+state-of-the-art+performance+on+several+benchmark+datasets+including+CIFAR100%252C+ImageNet100+and+TinyImageNet.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.5c9548f67ae2bb125cb60776fa973191%3frik%3dGcrJptWavO6ONg%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Accumulated+Learning&FORM=IRPRST&ck=A491887AB13EA5D5E0FE4B2FAA26828F&selectedIndex=34&itb=0)
