Training data for DALL·E 2 includes a vast collection of images paired with text descriptions to enable the model to understand correlations between visual and textual data. This diverse dataset helps DALL·E 2 learn to generate images based on given textual prompts with high accuracy.
Techniques used for training DALL·E 2 involve self-supervised learning, which allows the model to learn from unlabeled data without requiring human annotation. Moreover, transformer-based models, such as GPT-3, are utilized to process textual inputs effectively and generate coherent visual outputs.
Large-scale datasets are crucial for training DALL·E 2 as they provide a wide variety of examples for the model to learn from, leading to improved performance and generalization capabilities.
