Active learning represents a transformative paradigm in machine learning, aimed at reducing the annotation burden by selectively querying the most informative data points. This approach leverages ...

Label Distribution Learning (LDL) is a new learning paradigm to deal with label ambiguity and many researches have achieved the prominent performances. Compared with traditional supervised learning ...

How much time is your machine learning team spending on labeling data — and how much of that data is actually improving model performance? Creating effective training data is a challenge that many ML ...

Active Learning has been referred to as many things, including “project-based learning” and “flipped classes.” The fundamental premise of active learning is the replacement of passive class time with ...

During the past six months, we have witnessed some incredible developments in AI. The release of Stable Diffusion forever changed the artworld, and ChatGPT-3 shook up the internet with its ability to ...