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Unsupervised learning of feature representations is a challenging yetimportant problem for analyzing a large collection of multimedia data that donot have semantic labels. Recently proposed neural network-based unsupervisedlearning approaches have succeeded in obtaining features appropriate forclassification of multimedia data. However, unsupervised learning of featurerepresentations adapted to content-based matching, comparison, or retrieval ofmultimedia data has not been explored well. To obtain such retrieval-adaptedfeatures, we introduce the idea of combining diffusion distance on a featuremanifold with neural network-based unsupervised feature learning. This idea isrealized as a novel algorithm called DeepDiffusion (DD). DD simultaneouslyoptimizes two components, a feature embedding by a deep neural network and adistance metric that leverages diffusion on a latent feature manifold,together. DD relies on its loss function but not encoder architecture. It canthus be applied to diverse multimedia data types with their respective encoderarchitectures. Experimental evaluation using 3D shapes and 2D imagesdemonstrates versatility as well as high accuracy of the DD algorithm. Code isavailable at https://github.com/takahikof/DeepDiffusion

DeepDiffusion: Unsupervised Learning of Retrieval-Adapted Representations via Diffusion-Based Ranking on Latent Feature Manifold