Foundation models refer to a class of large-scale machine learning models that are trained on a broad range of data sources to acquire a wide set of capabilities. These models can be adapted or fine-tuned for various specific tasks and applications. Some examples are CLIP or GPT-4 by OpenAI. An important feature of these models is multimodality.
Apple tree surrounded by drones generated by Stable Diffusion XL.
Multimodality in machine learning refers to models that can understand, interpret, or generate data from multiple different modalities, such as text, images, audio, and video. The relationship between foundation models and multimodality is significant:
- Training on Diverse Data: Foundation models are often trained on diverse multi-modal datasets. This training enables them to develop a deep understanding of different types of data and the relationships between them.
- Transfer Learning and Adaptability: These models, due to their size and scope of training, can be fine-tuned for specific tasks across different modalities. For example, a model initially trained on text data can be adapted for image recognition or vice versa.
- Cross-Modal Understanding: Foundation models are increasingly capable of cross-modal understanding, such as understanding a concept expressed in text and translating it into an image (as seen in models like DALL-E), or generating text descriptions from images.
Multimodal training with contrastive learning (Created by OpenAI)
CLIP can be combined with a linear model by a technique called linear probe. This method is often applied to understand how well a deep learning model, especially one involved in unsupervised or self-supervised learning, has captured useful features in its hidden layers. A linear probe involves training a simple linear model, like logistic regression, on the features extracted from one of the layers of a pre-trained neural network. The objective is to see how well these features can be used for a specific task, such as classification. Since linear models can’t capture complex patterns, high performance indicates that the complexity is already captured in the features themselves, not in the classification model. For instance, in the image below, CLIP has improved the 2-D representation of a binary image classification problem. Check the complete notebook.
This figure represents the 2-D representation of a binary image classification problem. The image on the left side without using CLIP by extracting the most relevant features. The second one uses CLIP.
We’re only beginning to explore the depth of possibilities in this area! There’s an abundance of untapped potential waiting to be discovered; who knows, the next major advancement might be right on the horizon.
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