The Inventors have developed new methods in the application of machine learning techniques that allow for significant extension of current convolutional deep learning networks. These networks are used widely for commercial purposes including image classification/artificial vision and speech recognition.
Animal brains are far superior to present supervised learning algorithms in their ability to learn novel items and develop recognition from just a few labeled examples. A new frontier for machine learning and its applications is to form invariant representations of images and sounds (e.g. speech). These representations are unchanged despite transformations (e.g. translation, scale and rotation) to simplify learning and subsequent classification/recognition. The Inventors have developed a novel approach for automatic, unsupervised learning of transformations from unlabeled signals such as images and sounds.
This method is based on collecting a sequence (called an orbit) of each of n frames of k arbitrary objects/images/sounds/signals (called templates) undergoing a transformation. At run time, each signal to be recognized is represented by a signature, which is a vector comprised by the dot products of the signals with the n points of each of the k orbits. These measurements may be moments of the empirical distribution or other nonparametric estimates of it. In this way, a signal’s signature is selective and invariant with respect to a group of transformations.
For each template, the system can store all of its transformations and later obtain an invariant signature for new images without any explicit knowledge of the transformations. This implicit knowledge of the transformations allows the system to become automatically invariant to those transformations for new inputs as well as compute an invariant signature for a new object seen only once. This technique therefore allows for recognition from very few labeled examples and advances machine learning algorithms further toward human intelligence.
Saves labor cost of manually labeling large data
sets needed to train supervised learning algorithms
Method allows a drastic reduction in the
required number of labeled examples