Food image recognition using deep convolutional network with pre-training and fine-tuning

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How researchers see this paper

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Quantifying the Carbon Emissions of Machine Learning (2019, background): “Notably, recent research has shown that using pre-trained models with task-specific fine-tuning performs as well as training from scratch, while being more robust, for tasks in image recognition [7, 8] and NLP [9].”
Deep learning for image classification on very small datasets using transfer learning (2019, background): “However, they explore the transfer learning in person re-identification in large datasets like [27], [28], and [29].”
Deep-based Ingredient Recognition for Cooking Recipe Retrieval (2016, methodology): “Note that, although not using 1,000 food categories for pre-training, Arch-D still manages to outperform [36] by 3.”
A Survey on Food Computing (2018, background): “[Yanai and Kawano 2015] examined the effectiveness of the Alexnet network for food recognition task with pre-training and fine-tuning.”
Med3D: Transfer Learning for 3D Medical Image Analysis (2019, background): “Studies [9, 10, 11, 12, 13] have repeatedly proved that pre-trained models can accelerate the training convergence speed and increase the accuracy of the target model.”