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+are+among+the+most+widely+used+machine+learning+models+for+computer+vision+tasks%252C+such+as+image+classification.+To+improve+the+efficiency+of+CNNs%252C+many+CNNs+compressing+approaches+have+been+developed.+Low-rank+methods+approximate+the+original+convolutional+kernel+with+a+sequence+of+smaller+convolutional+kernels%252C+which+leads+to+reduced+storage+and+time+complexities.+In+this+study%252C+we+propose+a+novel+low-rank+CNNs+compression+method+that+is+based+on+reduced+storage+direct+tensor+ring+decomposition+(RSDTR).+The+proposed+method+offers+a+higher+circular+mode+permutation+flexibility%252C+and+it+is+characterized+by+large+parameter+and+FLOPS+compression+rates%252C+while+preserving+a+good+classification+accuracy+of+the+compressed+network.+The+experiments%252C+performed+on+the+CIFAR-10+and+ImageNet+datasets%252C+clearly+demonstrate+the+efficiency+of+RSDTR+in+comparison+to+other+state-of-the-art+CNNs+compression+approaches.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.567379acabdd68cc832743a2ffda7ee1%3frik%3dsAT6fGmJjC7zhw%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Tensor+Ring+Decomposition&FORM=IRPRST&ck=096F16A6D10B2961BEECBC8750B65369&selectedIndex=20&itb=0)
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