Author: Zen and the Art of Computer Programming
In the field of deep learning, supervised learning is an unsupervised learning method whose goal is to use labeled data to train the model parameters given the input. In this way, the model can be extracted from the input data. Patterns or features can be extracted and used for prediction in other tasks. Generally speaking, supervised learning relies on the quantity and quality of labeled data, especially when the amount of data is small or the distribution is uneven, it is necessary to overcome these problems by building more complex machine learning models. However, since various forms of data such as images, texts, and sounds in the real world are in the same information set, there are often mutual connections and intersections between different forms of data, so it is natural to establish a unified supervision system. The problem with learning models. Therefore, there are also many studies based on multimodal data modeling in the field of computer vision. So what is multimodal? Simply put, it refers to the situation where the dimension of the data is greater than two dimensions, that is, different types of data such as images, videos, texts, and voices form a whole. The processing of multimodal data has become a very important part of many applications. Data augmentation is an important research direction in the field of deep learning. It can help the training network to better fit the sample data and alleviate the risk of overfitting. For image data, the most common enhancement methods include cropping, flipping, rotating, scaling, filtering, etc. For text data, commonly used enhancement methods include character replacement, insertion, deletion, etc. So, for multimodal data, how can data augmentation be effectively performed? Data transfer learning (Transfer learning) is another landmark research direction, which can transfer knowledge in multiple fields to improve model performance. Earlier research showed that it is possible to fix the parameters of a deep neural network and then add an output layer on top so that it can classify new categories. However, as the depth of the network deepens, the number of parameters increases, which leads to the poor effect of transfer learning, because transfer learning only needs to learn a new output layer, not the entire neural network. because