📌 Masked Modelling Summary

Masked modelling is a technique used in machine learning where parts of the input data are hidden or covered, and the model is trained to predict these missing parts. This approach helps the model to understand the relationships and patterns within the data by forcing it to learn from the context. It is commonly used in tasks involving text, images, and other sequences where some information can be deliberately removed and then reconstructed.

🙋🏻‍♂️ Explain Masked Modelling Simply

Imagine reading a sentence with some words covered up and trying to guess what those words are based on the rest of the sentence. Masked modelling works in a similar way, helping computers get better at understanding language or images by practising filling in missing pieces. It is like a puzzle that trains the model to see the bigger picture even when some pieces are missing.

📅 How Can it be used?

Masked modelling can be used to train an AI system that automatically completes missing words in customer support emails.

🗺️ Real World Examples

In natural language processing, masked modelling is used to train language models like BERT. During training, some words in sentences are hidden, and the model learns to predict the missing words based on the surrounding text. This helps the model understand grammar and meaning, which improves its performance on tasks such as question answering and text summarisation.

In computer vision, masked modelling can be applied to image inpainting tasks where parts of an image are deliberately obscured. The model learns to reconstruct or fill in the missing sections, which can be useful for restoring old photographs or removing unwanted objects from pictures.

✅ FAQ

Masked modelling is a way for computers to learn by hiding some parts of the information they are given and asking them to guess what is missing. This helps the computer get better at understanding the overall picture, whether it is reading text, looking at images, or dealing with other types of data. It is a bit like playing a guessing game that helps the computer get smarter over time.

Masked modelling is used behind the scenes in many popular apps and services. For example, it helps make predictive text work on your phone, improves photo editing tools, and even assists in voice assistants. By learning from gaps in text or images, computers get better at tasks like translating languages or recognising objects in photos.

When parts of the data are hidden, the computer is forced to look at the remaining information and figure out the missing pieces. This encourages it to learn deeper connections and patterns in the data, making it more flexible and accurate when handling new or incomplete information in the future.

📚 Categories

• Artificial Intelligence
• Deep Learning
• Embeddings & Representations

🔗 External Reference Links

Masked Modelling link

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