Deep Auto-encoder Original /234 Image PCA /334 Deep Auto-encoder /234 1000 73A
Deep Auto-encoder Original Image PCA Deep Auto-encoder 784 784 784 1000 500 250 30 30 250 500 1000 784
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More:Contractive auto-encoder Ref:Rifai,Salah,et al."Contractive Auto-encoder auto-encoders:Explicit invariance during feature extraction."Proceedings of the 28th International Conference on De-noising auto-encoder Machine Learning(ICML-11).2011. As close as possible ◆ encode decode Add noise Vincent,Pascal,et al."Extracting and composing robust features with denoising autoencoders."/CML,2008
Auto-encoder • De-noising auto-encoder 𝑥 𝑥 ො 𝑐 encode decode Add noise 𝑥′ As close as possible More: Contractive auto-encoder Ref: Rifai, Salah, et al. "Contractive auto-encoders: Explicit invariance during feature extraction.“ Proceedings of the 28th International Conference on Machine Learning (ICML-11). 2011. Vincent, Pascal, et al. "Extracting and composing robust features with denoising autoencoders." ICML, 2008
Deep Auto-encoder-Example NN C 5 Encoder 3 8 6 0,° 878 8e4 23 。 PCA降到 32-dim 5 .3 Pixel -tSNE 0 89
Deep Auto-encoder - Example Pixel -> tSNE 𝑐 NN Encoder PCA 降到 32-dim
Auto-encoder Text Retrieval Vector Space Model Bag-of-word this is 1 word string: query “This is an apple'”e 0 an 1 apple 1 pen 0 document Semantics are not considered
Auto-encoder – Text Retrieval word string: “This is an apple” … this is a an apple pen 1 1 0 1 1 0 Bag-of-word Semantics are not considered. Vector Space Model document query