proxylabel

proxylabel is a term used in the field of machine learning, specifically within supervised learning tasks. It refers to a method of obtaining labels for data points that are not directly observed but are inferred from related information. This is particularly useful in scenarios where obtaining ground truth labels is expensive, time-consuming, or impractical.

The core idea behind proxylabel is to leverage a proxy, which is an indirectly related feature or a model trained on a different but correlated dataset, to predict the labels for the target data. For example, in image classification, instead of manually labeling every image, one might use labels from associated text descriptions as proxy labels. Another approach could involve using a pre-trained model on a similar task to generate initial labels, which are then refined or used directly.

Proxylabeling can be seen as a form of semi-supervised learning or weak supervision. The proxy labels are typically noisier and less accurate than directly human-annotated labels. Therefore, techniques are often employed to mitigate this label noise, such as robust loss functions, label refinement algorithms, or by treating the proxy labels as probabilistic estimates. The effectiveness of proxylabeling depends heavily on the quality of the proxy and the strength of its correlation with the true labels. This approach allows for the utilization of larger unlabeled datasets by generating approximate labels, thereby accelerating the development and deployment of machine learning models in data-scarce situations.