Hello everyone, I am Weixue AI, and today I will introduce you to Deep Learning Practice 33-Using the Zero-shot learning method to realize custom image categories and perform intelligent image classification. In deep learning, zero-shot (zero-shot learning) refers to making model predictions by solving related tasks without any training or learning samples for a specific task. Its goal is to make correct predictions or classifications from new samples that never appeared in the training data. The purpose of using zero-shot is to avoid repeated training of the model. For new classification problems, there is no need to train again, saving training time and improving development efficiency.
1. Zero-shot (zero-shot learning)
Zero-shot learning was first used in the NLP field, usually using a method based on word vector embedding. In this approach, words are embedded into a vector space with semantic information, and these vectors give their relationship in various dimensions. The relatedness of two words can then be inferred by comparing the distance between them in the vector space.
According to this idea, similar methods can also be used for other tasks, such as image classification, relationship inference, and personalized recommendation. In these tasks, the model compares "unknown" samples it has never seen with those "known" samples it has been trained on, and makes predictions or classifications based on their position in the vector space.
The advantage of zero-shot learning is that it can enhance the generalization ability of the model so that it can handle a wider range of tasks and scenarios. It has high development efficiency, and it should be used in combination with other technologies and methods in practical applications to improve the accuracy and robustness of predictions.
2. For Zero-shot learning model CLIP
The CLIP model is a model for zero-shot learning that can handle tasks such as natural language description and image classification without requiring task-specific fine-tuning. The CLIP model achieves zero-shot learning by pre-training images and text simultaneously.
There are two pre-training tasks for CLIP: use natural language for image descriptions, and use images to distinguish real