Multi-Modal learning refers to AI models that learn from multiple sources of data, such as text, images, and audio. By incorporating information from multiple modalities, these models can capture richer and more comprehensive representations. Multimodal Learning finds applications in areas like sentiment analysis, image captioning, and video understanding.
Variational Autoencoders are a type of generative model used in unsupervised learning. VAEs learn a low-dimensional representation of input data and can generate new…