Oversupervised

Oversupervised is a term sometimes used in machine learning to describe a scenario where a model is trained with more supervision than is strictly necessary or beneficial for its performance. This can manifest in several ways. One common situation is when a dataset has an excessive amount of highly detailed labels that might be redundant or even noisy. For example, if an image classification task is designed to identify cats and dogs, but the dataset also includes labels for the specific breed of cat or dog, this extra information could be considered oversupervision if the task only requires distinguishing between the two main categories.

Another aspect of oversupervision can arise from overly complex or rigidly defined training objectives. If the model is penalized too heavily for minor deviations from the exact training data or if the labels are so precise that they limit the model's ability to generalize to unseen variations, this can also be seen as oversupervision. In essence, the model becomes too specialized to the training set, hindering its robustness and adaptability. While supervision is crucial for training machine learning models, the concept of oversupervision suggests that an excess of it, or the wrong kind of supervision, can sometimes be detrimental, leading to diminished performance on real-world, uncurated data.