We explore an original strategy for building deep networks, based on stacking layers of denoising autoencoderswhich are trained locally to denoise corrupted versions of t heir inputs. The resulting algorithm is a straightforward variation on the stacking of ordinary autoencoders. It is however shown on a benchmark of classification problems to yield sign ificantly lower classification error, thus bridging the performance gap with deep belief networks (DBN), and in several cases surpassing it. Higher level representations learnt in this purely u ns pervised fashion also help boost the performance of subsequent SVM classifiers. Qualitative exp eriments show that, contrary to ordinary autoencoders, denoising autoencoders are able to lear n Gabor-like edge detectors from natural image patches and larger stroke detectors from digit images . Thi work clearly establishes the value of using a denoising criterion as a tractable unsupervised o bjective to guide the learning of useful higher level representations.