word-embeddings

When we are working with computer vision tasks, there are some scenarios where the amount of data (images) is small or not enough to reach acceptable performance. In addition, dealing with image data and Convolutional Neural Networks (CNN) is expensive in terms of computational power.Due to the issues aforementioned, in most cases it is convenient to use a technique called Transfer Learning, which consists of using models trained with millions of images, to improve the performance during the training process. We can implement this technique with Natural Language Processing (NLP) tasks, but instead of using pre-trained CNN models, for text classification, we are going to use pre-trained Word Embeddings.When we have so little data available to learn an appropriate task-specific embedding of ...