This paper introduces denoising autoencoders (DAEs), a method for learning robust features by training autoencoders to reconstruct clean inputs from corrupted ones. The key idea is to make the learned representations robust to partial input corruption, which helps in capturing stable structures in the data. This approach is motivated by the need to learn representations that are invariant to small irrelevant changes in the input, and it can be used to initialize deep architectures. The algorithm is motivated from both a manifold learning and information-theoretic perspective, as well as from a generative model perspective. The DAEs are trained by corrupting the input data and then learning to reconstruct the original input. This process helps in learning more abstract and robust representations. The algorithm is shown to be effective in improving the performance of deep architectures on classification tasks. Comparative experiments demonstrate that DAEs outperform traditional autoencoders in terms of classification accuracy on benchmark datasets. The paper also discusses the theoretical foundations of DAEs, including their relationship to other approaches in the literature. It shows that DAEs can be viewed as a form of variational inference in a generative model. Additionally, the paper presents experimental results on image classification tasks, showing that DAEs produce more useful feature detectors compared to traditional autoencoders. The results suggest that the denoising training process leads to better intermediate representations, which are more suitable for subsequent learning tasks. The study concludes that DAEs provide a promising approach for initializing deep neural networks and that further research is needed to explore other types of corruption processes.This paper introduces denoising autoencoders (DAEs), a method for learning robust features by training autoencoders to reconstruct clean inputs from corrupted ones. The key idea is to make the learned representations robust to partial input corruption, which helps in capturing stable structures in the data. This approach is motivated by the need to learn representations that are invariant to small irrelevant changes in the input, and it can be used to initialize deep architectures. The algorithm is motivated from both a manifold learning and information-theoretic perspective, as well as from a generative model perspective. The DAEs are trained by corrupting the input data and then learning to reconstruct the original input. This process helps in learning more abstract and robust representations. The algorithm is shown to be effective in improving the performance of deep architectures on classification tasks. Comparative experiments demonstrate that DAEs outperform traditional autoencoders in terms of classification accuracy on benchmark datasets. The paper also discusses the theoretical foundations of DAEs, including their relationship to other approaches in the literature. It shows that DAEs can be viewed as a form of variational inference in a generative model. Additionally, the paper presents experimental results on image classification tasks, showing that DAEs produce more useful feature detectors compared to traditional autoencoders. The results suggest that the denoising training process leads to better intermediate representations, which are more suitable for subsequent learning tasks. The study concludes that DAEs provide a promising approach for initializing deep neural networks and that further research is needed to explore other types of corruption processes.