This content originally appeared on DEV Community and was authored by Mike Young
This is a Plain English Papers summary of a research paper called Efficient Multimodal Learning Using Pre-Trained Models on a Single GPU. If you like these kinds of analysis, you should join AImodels.fyi or follow me on Twitter.
Overview
- The goal of multimodal alignment is to learn a single shared latent space between different input modalities, like images and text.
- Current powerful multimodal models require massive datasets and computational resources to train, making them inaccessible for many practical use cases.
- The authors propose FuseMix, a multimodal augmentation technique that can leverage pre-trained unimodal encoders to create effective multimodal models with much less data and compute.
Plain English Explanation
The researchers are working on a problem called multimodal alignment. The idea is to create a single “space” or representation that can capture the meanings and relationships between different types of input, like images and text. This shared space allows you to do …
Click here to read the full summary of this paper
This content originally appeared on DEV Community and was authored by Mike Young