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Zero-Shot Knowledge Distillation in Deep Networks
Gaurav Kumar Nayak · Konda Reddy Mopuri · Vaisakh Shaj · Venkatesh Babu Radhakrishnan · Anirban Chakraborty

Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #74

Knowledge distillation deals with the problem of training a smaller model (\emph{Student}) from a high capacity source model (\emph{Teacher}) so as to retain most of its performance. Existing approaches use either the training data or meta-data extracted from it in order to train the \emph{Student}. However, accessing the dataset on which the \emph{Teacher} has been trained may not always be feasible if the dataset is very large or it poses privacy or safety concerns (e.g., bio-metric or medical data). Hence, in this paper, we propose a novel data-free method to train the \emph{Student} from the \emph{Teacher}. Without even using any meta-data, we synthesize the \emph{Data Impressions} from the complex \emph{Teacher} model and utilize these as surrogates for the original training data samples to transfer its learning to \emph{Student} via knowledge distillation. We, therefore, dub our method ``Zero-Shot Knowledge Distillation" and demonstrate that our framework results in competitive generalization performance as achieved by distillation using the actual training data samples on multiple benchmark datasets.

Author Information

Gaurav Kumar Nayak (Indian Institute of Science)
Konda Reddy Mopuri (University of Edinburgh)
Vaisakh Shaj (University Of Lincoln)

I currently pursuing my PhD in Robot Learning under Prof Gerhard Neumann at the CLAS Lab, University Of Lincoln, UK. Before that I was a Data Scientist at the cybersecurity firm McAfee(Intel Security). Previously I worked with Intel for 2 years. I hold a post graduate degree in Machine Learning and Computing from the Indian Institute of Space Science and Technology.

Venkatesh Babu Radhakrishnan (Indian Institute of Science)
Anirban Chakraborty (Indian Institute of Science)

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