Learning Categories From Few Examples With Multi Model Knowledge Transfer

Apprenticeship learning with few examples

We consider the problem of imitation learning when the examples, provided by an expert human, are scarce. Apprenticeship Learning via Inverse Reinforcement Learning provides an efficient tool for generalizing the examples, based on the assumption that the expert’s policy maximizes a value function, which is a linear combination of state and action features. Most apprenticeship learning algorith...

Learning associative mappings from few examples

Biologically inspired Bayesian approach for learning object categories from few training examples

In this work we present a biologically inspired algorithm for learning object categories that uses Bayesian inference to integrate information within and across fixations. In our model, an object is represented as a collection of features of specific classes arranged at specific locations with respect to the location of the fixation point. Even though the number of feature detectors that we use...

Encoding Reusable Perceptual Features Enables Learning Future Categories from Few Examples

A perceptual system coping with a dynamic environment must be able to learn to detect new object categories from a few examples. However, learning from a small sample is restricted by the hindering effects of model overfitting. We present an algorithm aimed at circumventing the effects of overfitting by utilizing a set of reusable features, learned from several previously trained categories. We...

PSOM Network: Learning with Few Examples

Precise sensorimotor mappings between various motor, joint, sensor, and abstract physical spaces are the basis for many robotics tasks. Their cheap construction is a challenge for adaptive and learning methods. However, the practical application of many neural networks suffer from the need of large amounts of training data, which makes the learning phase a costly operation – sometimes beyond re...