Top suggestions for Contrastive Learning in AI ModelsExplore more searches like Contrastive Learning in AI ModelsPeople interested in Contrastive Learning in AI Models also searched for |
+presents+a+formidable+challenge+due+to+the+model+confusion+arising+from+multiple+intents+within+a+single+utterance.+While+previous+works+train+the+model+contrastively+to+increase+the+margin+between+different+multi-intent+labels%252C+they+are+less+suited+to+the+nuances+of+multi-intent+NLU.+They+ignore+the+rich+information+between+the+shared+intents%252C+which+is+beneficial+to+constructing+a+better+embedding+space%252C+especially+in+low-data+scenarios.+We+introduce+a+two-stage+Prediction-Aware+Contrastive+Learning+(PACL)+framework+for+multi-intent+NLU+to+harness+this+valuable+knowledge.+Our+approach+capitalizes+on+shared+intent+information+by+integrating+word-level+pre-training+and+prediction-aware+contrastive+fine-tuning.+We+construct+a+pre-training+dataset+using+a+word-level+data+augmentation+strategy.+Subsequently%252C+our+framework+dynamically+assigns+roles+to+instances+during+contrastive+fine-tuning+while+introducing+a+prediction-aware+contrastive+loss+to+maximize+the+impact+of+contrastive+learning.+We+present+experimental+results+and+empirical+analysis+conducted+on+three+widely+used+datasets%252C+demonstrating+that+our+method+surpasses+the+performance+of+three+prominent+baselines+on+both+low-data+and+full-data+scenarios.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.3a5c5d994149358f3cf96d499945fab6%3frik%3dMqX%252faYZLQgWfTQ%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Contrastive+Learning+in+AI+Models&simid=608047459035086528&FORM=IRPRST&ck=137FB3FCC998CA38EB0A5CEBC9067D2B&selectedIndex=7&itb=0)
+and+Adaptive+Outlier+Filter+(AdaOF).+The+first+one+gradually+creates+more+reliable+clusters+to+dynamically+refine+the+memory%252C+while+the+second+can+identify+and+filter+out+valuable+outliers+as+negative+samples.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.402b862cae8ac99ec8f4eaac774b7073%3frik%3dgcomYkpm1zQUzQ%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Contrastive+Learning+in+AI+Models&simid=608004479293791141&FORM=IRPRST&ck=A50D1D02AB012CA9D0A98E29D170432D&selectedIndex=10&itb=0)
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+aims+at+learning+a+semantic+segmentation+model+with+only+image-level+tags.+Despite+intensive+research+on+deep+learning+approaches+over+a+decade%252C+there+is+still+a+significant+performance+gap+between+WSSS+and+full+semantic+segmentation.+Most+current+WSSS+methods+always+focus+on+a+limited+single+image+(pixel-wise)+information+while+ignoring+the+valuable+inter-image+(semantic-wise)+information.+From+this+perspective%252C+a+novel+end-to-end+WSSS+framework+called+DSCNet+is+developed+along+with+two+innovations%3a+i)+pixel-wise+group+contrast+and+semantic-wise+graph+contrast+are+proposed+and+introduced+into+the+WSSS+framework%253B+ii)+a+novel+dual-stream+contrastive+learning+(DSCL)+mechanism+is+designed+to+jointly+handle+pixel-wise+and+semantic-wise+context+information+for+better+WSSS+performance.+Specifically%252C+the+pixel-wise+group+contrast+learning+(PGCL)+and+semantic-wise+graph+contrast+learning+(SGCL)+tasks+form+a+more+comprehensive+solution.+Extensive+experiments+on+PASCAL+VOC+and+MS+COCO+benchmarks+verify+the+superiority+of+DSCNet+over+SOTA+approaches+and+baseline+models.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.ac63b6d1b4e25bfd0b7a20196c73b97d%3frik%3d8bwBzW82Fcf0Rw%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Contrastive+Learning+in+AI+Models&simid=608052381073695747&FORM=IRPRST&ck=BFC677CAF510F60614A6C8142123784B&selectedIndex=22&itb=0)
%252C+doing+so+from+a+speech+signal+is+a+cumbersome+task.+To+address+this+challenge%252C+we+introduce+a+dual-encoder+architecture+that+facilitates+the+estimation+of+room+parameters+directly+from+speech+utterances.+During+pre-training%252C+one+encoder+receives+the+RIR+while+the+other+processes+the+reverberant+speech+signal.+A+contrastive+loss+function+is+employed+to+embed+the+speech+and+the+acoustic+response+jointly.+In+the+fine-tuning+stage%252C+the+specific+classification+task+is+trained.+In+the+test+phase%252C+only+the+reverberant+utterance+is+available%252C+and+its+embedding+is+used+for+the+task+of+room+shape+classification.+The+proposed+scheme+is+extensively+evaluated+using+simulated+acoustic+environments.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.75f56397505de7b7760ad660b0a8a28f%3frik%3du8zMK7TbfNQsOQ%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Contrastive+Learning+in+AI+Models&simid=608028101628674328&FORM=IRPRST&ck=68652EFA7667277CCA9854A41F6F88CA&selectedIndex=23&itb=0)
+methods+for+pneumonia+from+adult+chest+x-ray+(CXR)+images%252C+the+performance+of+CAD+methods+applied+to+pediatric+images+remains+suboptimal%252C+mainly+due+to+the+lack+of+large-scale+annotated+pediatric+imaging+datasets.+Establishing+a+proper+framework+to+leverage+existing+adult+large-scale+CXR+datasets+can+thus+enhance+pediatric+pneumonia+detection+performance.+In+this+paper%252C+we+propose+a+three-branch+parallel+path+learning-based+framework+that+utilizes+both+adult+and+pediatric+datasets+to+improve+the+performance+of+deep+learning+models+on+pediatric+test+datasets.+The+paths+are+trained+with+pediatric+only%252C+adult+only%252C+and+both+types+of+CXRs%252C+respectively.+Our+proposed+framework+utilizes+the+multi-positive+contrastive+loss+to+cluster+the+classwise+embeddings+and+the+embedding+similarity+loss+among+these+three+parallel+paths+to+make+the+classwise+embeddings+as+close+as+possible+to+reduce+the+effect+of+domain+shift.+Experimental+evaluations+on+open-access+adult+and+pediatric+CXR+datasets+show+that+the+proposed+method+achieves+a+superior+AUROC+score+of+0.8464+compared+to+0.8348+obtained+using+the+conventional+approach+of+join+training+on+both+datasets.+The+proposed+approach+thus+paves+the+way+for+generalized+CAD+models+that+are+effective+for+both+adult+and+pediatric+age+groups.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.d52faa1bb781a43b67a0db7043fcaafb%3frik%3d6IerGDbqNLdGcA%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Contrastive+Learning+in+AI+Models&simid=607992951656374990&FORM=IRPRST&ck=EA65C09E8E4D094A56B33996A935A71F&selectedIndex=24&itb=0)
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