Jun 10, 2024 · Zero-shot learning is a technique that enables pre-trained models to predict class labels of previously unknown data, i.e., data samples not present in the ...
Jun 28, 2024 · Explore zero-shot image classification, its significance, mechanics, benefits, challenges, applications, and future directions.
Jun 12, 2024 · This groundbreaking approach leverages pre-trained language models to transfer knowledge and adapt to new situations, making it a game-changer for applications ...
Jun 20, 2024 · Definition. Few-shot learning is a machine learning subfield in which models learn and make accurate predictions using only a small number of training examples.
6 days ago · In this paper, we propose the first framework for zero-shot video restoration and enhancement based on a pre-trained image diffusion model. ... zero-shot ...
Jun 18, 2024 · In this solution, we implement zero-shot NER with LLMs using the following key services: Amazon Textract – Extracts textual information from the input document.
Jun 12, 2024 · Zero-Shot Learning (ZSL) is a machine learning paradigm that enables models to classify data into categories they have never encountered during training. . If ...
7 days ago · This paper introduces a method for zero-shot video restoration using pre-trained image restoration diffusion models. Traditional video restoration methods often ...
Jun 28, 2024 · Zero-shot prompting enables models to generate responses to tasks they haven't been explicitly trained on, without any examples or fine-tuning. By leveraging ...
4 days ago · To accurately predict the effects of mutation, we first develop a deep representation learning model as a base module in ProMEP, which integrates both sequence ...