Instagram Hashtag Prediction Using Deep Neural Networks

Abstract

Instagram is one of the most popular photos sharing services. For more convenient content search people use hashtags (#nature, #love, etc.) in posts with photos. The author’s aim is to make hashtag prediction possible and convenient for users.

The paper provides a reader with a detailed theoretical overview of Multi-Label Image Classification, Knowledge Distillation, and an overview of ResNet architecture. Next, the author proposes improvements on ResNet architecture allowing the model to boost quality and converge faster. Finally, the model type Self-Improving-Modified-Resnet (SIMR) is presented. Their main feature is the additional bottleneck block used as the tool incorporating benefits from a combination of self-training and knowledge distillation.