Knowledge distillation is an effective model compression technique in which a smaller model is trained to mimic a larger pretrained model. However in order to make these compact models suitable for real world deployment, not only do
 we need to reduce the performance gap but also we need to make them more robust to commonly occurring and adversarial perturbations. Noise permeates every level of the nervous system, from the perception of sensory signals to the
 generation of motor responses. We therefore believe that noise could be a crucial element in improving neural networks training and addressing the apparently contradictory goals of improving both the generalization and robustness of the
 model. Inspired by trial-to-trial variability in the brain that can result from multiple noise sources, we introduce variability through noise at either the input level or the supervision signals. Our results show that noise can improve both the generalization and robustness of the model. ”Fickle Teacher” which uses dropout in teacher model as a source of response variation leads to significant generalization improvement. ”Soft Randomization”, which matches the output distribution of
 the student model on the image with Gaussian noise to the output of the teacher on original image, improves the adversarial robustness manifolds compared to the student model trained with Gaussian noise. We further show the surprising effect of random label corruption on a model’s adversarial robustness. The study highlights the benefits of adding constructive noise in the knowledge distillation framework and hopes to inspire further work in the area. The design of Deep Neural Networks (DNNs) for efficient real world deployment involves careful consideration of following key elements: memory and computational requirements, performance, reliability and security. DNNs are often deployed in resource constrained devices or in applications with strict latency requirements such as self driving cars which leads to a necessity for developing compact models that generalizes well. Furthermore, since the environment in which the models are deployed are often constantly changing, it is important to consider their performance on both indistribution data as well as out-of-distribution data. Thereby ensuring the reliability of the models under distribution shift. Finally, the model needs to be robust to malicious attacks by adversaries (Kurakin et al., 2016) . Many techniques have been proposed for achieving high performance in compressed model such as model quantization, model pruning, and knowledge distillation. In our study, we focus on knowledge distillation as an interactive learning method which is more similar to human learning. Knowledge Distillation involves training a smaller network (student) under the supervision of a larger pre-trained network (teacher). In the original formulation, Hinton et al. (2015) proposed mimicking the softened softmax output of the teacher model which consistently improves the performance of the student model compared to the model trained without teacher assistance. However, despite the promising performance gain, there is still a significant performance gap between the student and the teacher model. Consequently an optimal method of capturing knowledge from the larger network and transferring it to a smaller model remains an open question. While reducing this generalization gap is important, in order to truly make these models suitable for real world deployment, it is also pertinent to incorporate methods into the knowledge distillation framework that improve the robustness of the student model to both commonly occurring and malicious perturbations. For our proposed methods, we derive inspiration from studies in neuroscience on how humans learn. A human infant is born with billions of neurons and throughout the course of its life, the connections between these neurons are constantly changing. This neuroplasticity is at the very core of learning (Draganski et al., 2004) . Much of the learning for a child happens not in isolation but rather through collaboration. A child learns by interacting with the environment and understanding it through their own experience as well as observations of others. Two learning theories are central to our approach: cognitive bias and trial-to-trial response variation. Human decision-making shows systematic simplifications and deviations from the tenets of rationality ('heuristics') that may lead to sub-optimal decisional outcomes ('cognitive biases') (Korteling et al., 2018) . These biases are strengthened through repeatedly rewarding a particular response to the same stimuli. Trial-to-trial response variation in the brain, i.e. variation in neural responses to the same stimuli, encodes valuable information about the stimuli (Scaglione et al., 2011) . We hypothesize that introducing constructive noise in the student-teacher collaborative learning framework to mimic the trial-to-trial response variation in humans can act as a deterrent to cognitive bias which is manifested in the form of memorization and over-generalization in neural networks. When viewed from this perspective, noise can be a crucial element in improving learning and addressing the apparent contradictory goals of achieving accurate and robust models. In this work, we present a compelling case for the beneficial effects of introduction of noise in knowledge distillation. We provide a comprehensive study on the effects of noise on model generalization and robustness. Our contributions are as follows: • A comprehensive analysis on the effects of adding a diverse range of noise types in different aspects of the teacher-student collaborative learning framework. Our study aims to motivate further work in exploring how noise can improve both generalization and robustness of the student model. • A novel approach for transferring teacher model's uncertainty to a student using Dropout in teacher model as a source of trial-to-trial response variability which leads to significant generalization improvement. We call this method "Fickle Teacher". • A novel approach for using Gaussian noise in the knowledge distillation which improves the adversarial robustness of the student model by an order of magnitude while significantly limiting the drop in generalization. we refer to this method as "Soft Randomization". • Random label corruption as a strong deterrent to cognitive bias and demonstrating its surprising ability to significantly improve adversarial robustness with minimal reduction in generalization. Inspired by trial-to-trial variability in the brain, we introduce variability in the knowledge distillation framework through noise at either the input level or the supervision signals. For this purpose, we proposed novel ways of introducing noise at multiple levels and studied their effect on both generalization and robustness. Fickle teacher improves the both in-distribution and out of distribution generalization significantly while also slightly improving robustness to common and adversarial perturbations. Soft randomization improves the adversarial robustness of the student model trained alone with Gaussian noise by a huge margin for lower noise intensities while also reducing the drop in generalization. We also showed the surprising effect of random label corruption alone in increasing the adversarial robustness by an order of magnitude in addition to improving the generalization. Our strong empirical results suggest that injecting noises which increase the trial-to-trial variability in the knowledge distillation framework is a promising direction towards training compact models with good generalization and robustness. A APPENDIX
Inspired by trial-to-trial variability in the brain that can result from multiple noise sources, we introduce variability through noise in the knowledge distillation framework and studied their effect on generalization and robustness.
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