CRITICAL ANALYSIS OF SELF-SUPERVISION

Presented by

Maral Rasoolijaberi

Introduction

This paper presents

Critiques

By using nearly 5 million parameters, GoogLeNet represented nearly a 12 times reduction in terms of parameters compared it the previous architectures like VGGNet, AlexNet. This enabled Inception network to be used for many big data applications where a huge amount of data was needed to be processed at a reasonable cost while the computational capacity was limited. However, the inception network is still complex and susceptible to scaling. If the network is scaled up, large parts of the computational gains can be lost immediately. Also there was no clear description about the various factors that lead to the design decision of this inception architecture, making it harder to adapt to other applications while maintaining the same computational efficiency.

References

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Footnote 1: Hebbian theory is a neuroscientific theory claiming that an increase in synaptic efficacy arises from a presynaptic cell's repeated and persistent stimulation of a postsynaptic cell. It is an attempt to explain synaptic plasticity, the adaptation of brain neurons during the learning process.

Footnote 2: Fore more explanation on 1 by 1 convolution refer to: https://iamaaditya.github.io/2016/03/one-by-one-convolution/