Learning to hash: a comprehensive survey of deep learning-based hashing methods

Explosive growth of big data demands efficient and fast algorithms for nearest neighbor search. Deep learning-based hashing methods have proved their efficacy to learn advanced hash functions that suit the desired goal of nearest neighbor search in large image-based data-sets. In this work, we present a comprehensive review of different deep learning-based supervised hashing methods particularly for image data-sets suggested by various researchers till date to generate advanced hash functions. We categorize prior works into a five-tier taxonomy based on: (i) the design of network architecture, (ii) training strategy based on nature of data-set, (iii) the type of loss function, (iv) the similarity measure and, (v) the nature of quantization. Further, different data-sets used in prior works are reported and compared based on various challenges in the characteristics of images that are part of the data-sets. Lastly, different future directions such as incremental hashing, cross-modality hashing and guidelines to improve design of hash functions are discussed. Based on our comparative review, it has been observed that generative adversarial networks-based hashing models outperform other methods. This is due to the fact that they leverage more data in the form of both real world and synthetically generated data. Furthermore, it has been perceived that triplet-loss-based loss functions learn better discriminative representations by pushing similar patterns together and dis-similar patterns away from each other. This study and its observations shall be useful for the researchers and practitioners working in this emerging research field.

     

A joint feature selection framework for multivariate resource usage prediction in cloud servers using stability and prediction performance

Resource provisioning in cloud servers depends on future resource utilization of different jobs. As resource utilization trends vary dynamically, effective resource provisioning requires prediction of future resource utilization. The problem becomes more complicated as performance metrics related to one resource may depend on utilization of other resources also. In this paper, different multivariate frameworks are proposed for improving the future resource metric prediction in cloud. Different techniques for identifying the set of resource metrics relevant for the prediction of desired resource metric are analyzed. The proposed multivariate feature selection and prediction frameworks are validated for CPU utilization prediction in Google cluster trace. Joint analysis based on the prediction performance of the multivariate framework as well as its stability is used for selecting the most suitable feature selection framework. The results of the joint analysis indicate that features selected using the Granger causality technique perform best for multivariate resource usage prediction.