What does "Data-based Methods" mean?
Table of Contents
Data-based methods focus on how we can use existing information to improve results in various tasks. These methods often involve learning from a specific set of data to help make predictions or decisions about new, unseen data.
Single Domain Generalization
Single domain generalization deals with the challenge of working with only one source of data. It tries to ensure that the skills learned from this data can still be useful when faced with different conditions. One approach to solve this problem is to separate the important features related to the task from those that are specific to the data source. This helps maintain strong performance even when the data changes.
Source-Free Domain Adaptation
Source-free domain adaptation is a newer method that helps adapt to new data without needing access to the original data set. Instead, it relies on a model that has already learned from the original data, using new unlabeled data to adjust and improve its performance. This approach is important because, in real life, we often cannot access older data due to privacy reasons.
Importance of Adaptation
Both methods highlight the importance of adjusting to new information. Accurate predictions in changing conditions can lead to better outcomes in fields like healthcare or business. By improving how models adapt to new data, we can achieve higher performance in tasks such as image analysis or classification.