Keywords: unsupervised learning, object recognition, sparse coding, convolutional networks Abstract:Image processing and recognition has traditionally relied on hard-wired features and trainable classifiers. The next challenge of computer vision, machine learning, and image processing, is to devise methods that can automatically learn feature extractors and high-level image representations from labeled and unlabeled data. The set of methods collectively known as "Deep Learning" is an attempt to learn hierarchies of features with multiple levels of abstraction, and suitable invariances. I will describe several deep learning methods, some of which involve new forms of sparse coding. Specific model architectures for image recognition, based on stacks on non-linear filter banks, and trained with these methods will be described. A number of applications to object dectection, object recognition, and vision-based navigation for mobile robots will be shown.