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+have+become+a+cornerstone+in+generative+modeling+and+representation+learning+within+machine+learning.+This+paper+explores+a+nuanced+aspect+of+VAEs%252C+focusing+on+interpreting+the+Kullback-Leibler+(KL)+Divergence%252C+a+critical+component+within+the+Evidence+Lower+Bound+(ELBO)+that+governs+the+trade-off+between+reconstruction+accuracy+and+regularization.+Meanwhile%252C+the+KL+Divergence+enforces+alignment+between+latent+variable+distributions+and+a+prior+imposing+a+structure+on+the+overall+latent+space+but+leaves+individual+variable+distributions+unconstrained.+The+proposed+method+redefines+the+ELBO+with+a+mixture+of+Gaussians+for+the+posterior+probability%252C+introduces+a+regularization+term+to+prevent+variance+collapse%252C+and+employs+a+PatchGAN+discriminator+to+enhance+texture+realism.+Implementation+details+involve+ResNetV2+architectures+for+both+the+Encoder+and+Decoder.+The+experiments+demonstrate+the+ability+to+generate+realistic+faces%252C+offering+a+promising+solution+for+enhancing+VAE-based+generative+models.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.00ce256754ff6ac55499982e16448dc2%3frik%3dlIRNZ7zzLbi2NQ%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Vae+Based+Sensor+Application+and+Regression&FORM=IRPRST&ck=CA866A6A24AFF0C9274955E9D343A567&selectedIndex=26&itb=0)
