+enables+machine+learning+models+to+learn+from+continuously+shifting+new+training+data+in+absence+of+data+from+old+tasks.+Recently%252C+pretrained+vision+transformers+combined+with+prompt+tuning+have+shown+promise+for+overcoming+catastrophic+forgetting+in+CL.+These+approaches+rely+on+a+pool+of+learnable+prompts+which+can+be+inefficient+in+sharing+knowledge+across+tasks+leading+to+inferior+performance.+In+addition%252C+the+lack+of+fine-grained+layer+specific+prompts+does+not+allow+these+to+fully+express+the+strength+of+the+prompts+for+CL.+We+address+these+limitations+by+proposing+ConvPrompt%252C+a+novel+convolutional+prompt+creation+mechanism+that+maintains+layer-wise+shared+embeddings%252C+enabling+both+layer-specific+learning+and+better+concept+transfer+across+tasks.+The+intelligent+use+of+convolution+enables+us+to+maintain+a+low+parameter+overhead+without+compromising+performance.+We+further+leverage+Large+Language+Models+to+generate+fine-grained+text+descriptions+of+each+category+which+are+used+to+get+task+similarity+and+dynamically+decide+the+number+of+prompts+to+be+learned.+Extensive+experiments+demonstrate+the+superiority+of+ConvPrompt+and+improves+SOTA+by+%7e3%2525+with+significantly+less+parameter+overhead.+We+also+perform+strong+ablation+over+various+modules+to+disentangle+the+importance+of+different+components.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.43292002dc0aa19d15dad6c76cc3f422%3frik%3dzxcwn%252bcozu9lDg%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Continual+Learning+Ai&simid=608017703390435536&FORM=IRPRST&ck=11CED19C8241B7EA39D29EB868CF9A62&selectedIndex=33&itb=0)
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