Transfer Learning and Convolutional Neural Networks (CNN)

While leading Neural Network architectures for NLP are helmed by Transformers (since 2017, with the paper "Attention Is All you Need"), Computer Vision progress has been led by Convolutional Neural Networks (CNN) ever since AlexNet became the first CNN winner of the ImageNet challenge in 2012 -- though its supremacy has been contested by Vision Transformers (ViT) since 2020. From 2012 onward, many variations of CNN have been developed to tackle different Computer Vision problems, such as Instance Segmentation and Objection Detection. Before the age of CNN, the simple approach to Computer Vision is to treat images pixels as individual features to feed into the deep neural networks (Multi-Layer Perceptrons). Fortunately, the ingenious CNN architecture comes to the rescue. In this article, we will explore basic CNN architecture, and then utilize Transfer Learning with CNN-- borrowing state-of-the-art architectures with pre-trained weights fine-tuned on ImageNet -- to achieve cutting-edge results on predicting a Kaggle's Cat versus Dog dataset.