Joint Visual Denoising and Classification using Deep Learning
This work addresses the problem of improving visual recognition in noisy conditions for handwritten image analysis, representing an incremental advance over separate pipeline methods.
The authors tackled the problem of visual denoising and classification by proposing a joint framework instead of a traditional pipeline, achieving at least 20% better classification accuracy on corrupted MNIST and USPS datasets and producing clearer recovered images.
Visual restoration and recognition are traditionally addressed in pipeline fashion, i.e. denoising followed by classification. Instead, observing correlations between the two tasks, for example clearer image will lead to better categorization and vice visa, we propose a joint framework for visual restoration and recognition for handwritten images, inspired by advances in deep autoencoder and multi-modality learning. Our model is a 3-pathway deep architecture with a hidden-layer representation which is shared by multi-inputs and outputs, and each branch can be composed of a multi-layer deep model. Thus, visual restoration and classification can be unified using shared representation via non-linear mapping, and model parameters can be learnt via backpropagation. Using MNIST and USPS data corrupted with structured noise, the proposed framework performs at least 20\% better in classification than separate pipelines, as well as clearer recovered images. The noise model and the reproducible source code is available at {\url{https://github.com/ganggit/jointmodel}}.