The internal inspection of fabrics is one the most important phases production in order to achieve high quality standard textile industry. Therefore, developing efficient automatic control mechanism has been an extremely major area research. In this paper, famous architecture Googlenet was fine-tuned into two configurations for texture defect classification that trained on a database (TILDA). e...

Perceptual learning, a process in which training improves visual discrimination, is often specific to the trained retinal location, and this location specificity is frequently regarded as an indication of neural plasticity in the retinotopic visual cortex. However, our previous studies have shown that "double training" enables location-specific perceptual learning, such as Vernier learning, to ...

84 AI MAGAZINE As evidenced by the articles in this special issue, transfer learning has come a long way in the past five or so years, partially because of DARPA’s Transfer Learning program, which sponsored much of the work reported in this issue.1 Work conducted as part of this program and work conducted under other auspices, as evidenced by the many conference and workshop papers,2 demonstrat...

We propose a novel unsupervised transfer learning framework that utilises unlabelled auxiliary data to quantify and select the most relevant transferrable knowledge for recognising a target object class from the background given very limited training target samples. Unlike existing transfer learning techniques, our method does not assume that auxiliary data are labelled, nor the relationships b...

Multitask Learning is an inductive transfer method that improves generalization by using domain information implicit in the training signals of related tasks as an inductive bias. It does this by learning multiple tasks in parallel using a shared representation. Mul-titask transfer in connectionist nets has already been proven. But questions remain about how often training data for useful extra...

We present three different learning mechanisms for transferring spatial knowledge from one problem to another, related problem: memorybased transfer, search-based transfer, and transfer using reinforcement learning. We describe the approaches and present preliminary results demonstrating successful transfer using these approaches in Soar and testing them in the Urban Combat Testbed.

Transfer learning algorithms are used when one has sufficient training data for one supervised learning task (the source task) but only very limited training data for a second task (the target task) that is similar but not identical to the first. These algorithms use varying assumptions about the similarity between the tasks to carry information from the source to the target task. Common assump...