Model distillation is not to be confused with model compression, which describes methods to decrease the size of a large model itself, without training a new model.

Dec 6, 2024 · Distillation is a technique designed to transfer knowledge of a large pre-trained model (the "teacher") into a smaller model (the "student"), enabling the student model to achieve comparable …

Feb 19, 2025 · Model distillation (or knowledge distillation) is a technique designed to condense the capabilities and thought processes of a large, pre-trained “teacher” model into a smaller “student” …

Sep 8, 2025 · Model distillation is a process in artificial intelligence in which knowledge from the big “teacher” model is transferred to the smaller, faster “student” model. The main aim here is to create a …

Model distillation happens to be a process for transferring knowledge from a large complex model to a smaller efficient model. There exist unique types of model distillation that cater to various …

Jun 8, 2024 · Model distillation is a technique used to create smaller, faster models that retain much of the performance of a larger, more complex model. The larger model is referred to as the “teacher,” …

Jul 23, 2025 · Knowledge Distillation is a model compression technique in which a smaller, simpler model (student) is trained to imitate the behavior of a larger, complex model (teacher).

Sep 1, 2023 · It’s used in deep learning as a form of model compression and knowledge transfer, particularly for massive deep neural networks. The goal of knowledge distillation is to train a more …

Oct 1, 2024 · Model distillation involves fine-tuning smaller, cost-efficient models using outputs from more capable models, allowing them to match the performance of advanced models on specific …

Model distillation is a powerful technique in machine learning for compressing large, complex models (the "teacher") into smaller, more efficient models (the "student").