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Deep Convolutional features extracted from a comprehensive labeled dataset,contain substantial representations which could be effectively used in a newdomain. Despite the fact that generic features achieved good results in manyvisual tasks, fine-tuning is required for pretrained deep CNN models to be moreeffective and provide state-of-the-art performance. Fine tuning using thebackpropagation algorithm in a supervised setting, is a time and resourceconsuming process. In this paper, we present a new architecture and an approachfor unsupervised object recognition that addresses the above mentioned problemwith fine tuning associated with pretrained CNN-based supervised deep learningapproaches while allowing automated feature extraction. Unlike existing works,our approach is applicable to general object recognition tasks. It uses apretrained (on a related domain) CNN model for automated feature extractionpipelined with a Hopfield network based associative memory bank for storingpatterns for classification purposes. The use of associative memory bank in ourframework allows eliminating backpropagation while providing competitiveperformance on an unseen dataset.

Unsupervised Learning using Pretrained CNN and Associative Memory Bank