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Transfer Learning ConvlutionalNeural Network
+in+quantifying+the+effective+connectivity+between+artificial+neurons.+In+a+feedforward+network%252C+the+TE+can+be+used+to+quantify+the+relationships+between+neuron+output+pairs+located+in+different+layers.+Our+focus+is+on+how+to+include+the+TE+in+the+learning+mechanisms+of+a+Convolutional+Neural+Network+(CNN)+architecture.+We+introduce+a+novel+training+mechanism+for+CNN+architectures+which+integrates+the+TE+feedback+connections.+Adding+the+TE+feedback+parameter+accelerates+the+training+process%252C+as+fewer+epochs+are+needed.+On+the+flip+side%252C+it+adds+computational+overhead+to+each+epoch.+According+to+our+experiments+on+CNN+classifiers%252C+to+achieve+a+reasonable+computational+overhead--accuracy+trade-off%252C+it+is+efficient+to+consider+only+the+inter-neural+information+transfer+of+a+random+subset+of+the+neuron+pairs+from+the+last+two+fully+connected+layers.+The+TE+acts+as+a+smoothing+factor%252C+generating+stability+and+becoming+active+only+periodically%252C+not+after+processing+each+input+sample.+Therefore%252C+we+can+consider+the+TE+is+in+our+model+a+slowly+changing+meta-parameter.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.2441a6e07ffa91774d6703544db9c18e%3frik%3duHoVKT4QQrKG2Q%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Transfer+Learning+Convlutional+Neural+Network&FORM=IRPRST&ck=942E04CB76F12194529DAD3D48665213&selectedIndex=17&itb=0)
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