Multi-Resolution Learning for Knowledge Transfer

Related objects may look similar at low-resolutions; differences begin to emerge naturally as the resolution is increased. By learning across multiple resolutions of input, knowledge can be transfered between related objects. My dissertation develops this idea and applies it to the problem of multitask transfer learning. Thesis Overview Consider a child learning about farm animals. A common early mistake for children is confusing cows with horses, dogs with cats, and so forth. There are many similarities between these animals from a general perspective, enough so that a lot of knowledge learned about one animal can transfer to other similar animals (cows and horses both have four legs, are large, eat grass). At more detailed perspectives, we begin to notice significant differences between these animals that requires learning about each animal individually (horses have manes, cows have udders). The relationship between objects emerges naturally from viewing those objects at varying res...

• Type: Conference
• Year: 2006
• Where: AAAI
• Authors: Eric Eaton
• Keywords: AAAI 2006, Intelligent Agents, Multiple Resolutions, Related Objects, Transfer Learning