Deep Neural Network Retrieval

With the rapid development of deep learning-based techniques, the general public can use a lot of "machine learning as a service" (MLaaS), which provides end-to-end machine learning solutions. Taking the image classification task as an example, users only need to update their dataset and labels to MLaaS without requiring the specific knowledge of machine learning or a concrete structure of the classifier. Afterward, MLaaS returns a well-trained classifier to them. In this paper, we explore a potential novel task named "deep neural network retrieval" and its application which helps MLaaS to save computation resources. MLaaS usually owns a huge amount of well-trained models for various tasks and datasets. If a user requires a task that is similar to the one having been finished previously, MLaaS can quickly retrieve a model rather than training from scratch. We propose a pragmatic solution and two different approaches to extract the semantic feature of DNN representing the function of DNN, which is analogous to the usage of word2vec in natural language processing. The semantic feature of DNN can be expressed as a vector by feeding some well-designed litmus images into the DNN or as a matrix by reversely constructing the most desired input of DNN. Both methods can consider the topological information and parameters of the DNN simultaneously. Extensive experiments, including multiple datasets and networks, also demonstrate the efficiency of our method and show the high accuracy of deep neural network retrieval.