We study unsupervised learning by developing introspective generative modeling (IGM) that attains a generator using progressively learned deep convolutional neural networks. The generator is itself a discriminator, capable of introspection: being able to self-evaluate the difference between its generated samples and the given training data. When followed by repeated discriminative learning, des...

1 Abstract Introspection in computer systems enables a computer to examine its own computational behavior. Knowledge gained by introspection can be used by the computer to optimize itself and to explain its own behavior to other computers or to human observers. In increasingly complex computer systems, introspective computers can be applied to managing their own complexity. A computer capable o...

Traditional learning techniques have the assumption that training and test data are drawn from the same data distribution, and thus they are not suitable for dealing with the situation where new unlabeled data are obtained from fast evolving, related but different information sources. This leads to the crossdomain learning problem which targets on adapting the knowledge learned from one or more...

Given an arbitrary learning situation, it is difficult to determine the most appropriate learning strategy. The goal of this research is to provide a general representation and processing framework for introspective reasoning for strategy selection. The learning framework for an introspective system is to perform some reasoning task. As it does, the system also records a trace of the reasoning ...

This essay is about two familiar theses in the philosophy of mind: constitutivism instrumental desires, and introspective beliefs, arguments for against them. Constitutivism desire thesis that desires are at least partly constituted by means-end beliefs which explain them, a has been championed most prominently Michael Smith. belief mental states they about, Sydney Shoemaker. Despite their simi...

This paper explores the suitability of commonly employed classification methods to action-selection tasks in robotics, and argues that a classifier’s introspective capacity is a vital but as yet largely under-appreciated attribute. As illustration we propose an active learning framework for semantic mapping in mobile robotics and demonstrate it in the context of autonomous driving. In this fram...

Recently more and more researchers have been supporting the view that learning is a goaldriven process. One of the key properties of a goal-driven learner is introspectiveness the ability to notice the gaps in its knowledge and to reason about the information required to fill in those gaps. In this paper, we introduce a quantitative introspective learning paradigm into case-based reasoning (CBR...

This report was written in order to complete the requirements for my Master's degree, under supervision of prof. In this report, an introspective machine, capable to pass judgement on its own deductive performances, is modelled and analyzed. First, the class of ideal machines which is provided with unlimited resources is studied. Since ideal introspective machines are usually unfeasible, their ...