Incremental model learning for spectroscopy-based food analysis

Model Development and Incremental Learning Based on Case-Based Reasoning for Signal and Image Analysis

RKHS-based functional analysis for exact incremental learning

We investigate the problem of incremental learning in arti"cial neural networks by viewing it as a sequential function approximation problem. A framework for discussing the generalization ability of a trained network in the original function space using tools of functional analysis based on reproducing kernel Hilbert spaces (RKHS) is introduced. Using this framework, we devise a method of carry...

An Incremental Learning Model for Commonsense Reasoning

A self-organizing incremental learning model that attempts to combine inductive learning with prior knowledge and default reasoning is described. The inductive learning scheme accounts for useful generalizations and dynamic priority allocation, and effectively supplements prior knowledge. New rules may be created and existing rules modified, thus allowing the system to evolve over time. By comb...

FearNet: Brain-Inspired Model for Incremental Learning

Incremental class learning involves sequentially learning classes in bursts of examples from the same class. This violates the assumptions that underlie methods for training standard deep neural networks, and will cause them to suffer from catastrophic forgetting. Arguably, the best method for incremental class learning is iCaRL, but it requires storing training examples for each class, making ...

Automatic Incremental Model Learning for Scene Interpretation

In this paper, we investigate automatic model learning for the interpretation of complex scenes with structured objects. We present a learning, interpretation, and evaluation cycle for processing such scenes. By including learning and interpretation in one framework, an evaluation and feedback learning is enabled that takes interpretation challenges like context and combination of diverse types...