Transfer Learning for Image Clustering

Abstract

As the development of deep learning models are getting more and more popular, for industries, building good deep learning models case by case is still very expensive. At the meantime, with more and more models are being published to the public through different platforms, transfer learning which is used for transferring knowledge (i.e., the feature encoding) from the pre- trained model to a new model is becoming a technology for practical usage. This work introduces an approach to use a pre- trained deep learning model to compute features for downstream models. As an application of this approach, it demonstrate how to use transfer learning to improve training performance for image clustering by combining transfer learning for vectorization of image data and k-means for image clustering. To demonstrate the effect, we used the tf_flowers dataset, and ResNet50 for featurization of images.