Incremental Learning of Object Detectors using Limited Training Data

Abstract

State of the art Deep learning models, despite being at par to human level in some of the challenging tasks, still suffer badly when they are put in the condition where they have to learn with the passage of time. This open chal lenge problem of making deep learning model learn with the passage of time is often called with synonymous names like Lifelong Learning, Incremental Learning or Continual Learning etc. In each increment new classes / tasks are introduced to the existing model and trained on them while maintaining the accuracy on the previously learnt classes / tasks. But accuracy of the deep learning model on the previously learnt classes / tasks decreases with each increment. Main reason behind this accuracy drop is catastrophic forgetting, an inherent flaw in the deep learning models, where weights learnt during the past increments, get disturbed while learning the new classes / tasks from new increment. Approaches have been proposed to mitigate or avoid this catastrophic forgetting, such as use of knowledge distillation, rehearsal over previous classes, or dedicated paths for different increments etc. Here in my work I proposed a novel approach based on transfer learn ing methodology, which uses a combination of pre-trainied shared and fixed network as backbone, along with a dedicated network extension in incremen tal setting for the learning of new tasks incrementally. Results have shown that proposed architecture successfully bypasses the catastrophic forgetting issue and completely eradicate the need of saved exemplars or retraining phases which are required by the current state of the art model to maintain performance, and still have performance comparable to the state of the art incremental learning model.