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M-Theory (learning framework)
In Machine Learning and Computer Vision, M-Theory is a learning framework inspired by feed-forward processing in the ventral stream of visual cortex and originally developed for recognition and classification of objects in visual scenes. M-Theory was later applied to other areas, such as speech recognition. On certain image recognition tasks, algorithms based on a specific instantiation of M-Theory, HMAX, achieved human-level performance.〔Serre T., Oliva A., Poggio T. (2007) A feedforward architecture accounts for rapid categorization. ''PNAS'', vol. 104, no. 15, pp. 6424-6429〕
The core principle of M-Theory is extracting representations invariant to various transformations of images (translation, scale, 2D and 3D rotation and others). In contrast with other approaches using invariant representations, in M-Theory they are not hardcoded into the algorithms, but learned. M-Theory also shares some principles with Compressed Sensing. The theory proposes multilayered hierarchical learning architecture, similar to that of visual cortex.
==Intuition==
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