Continual Learning vs. Active Learnin

Continual Learning vs. Active Learning

Differences between continual learning and active learning are

1. Definition: Continual learning refers to the ability of a model or system to learn from new data over time while retaining knowledge from previous experiences. Active learning, on the other hand, is a subset of supervised machine learning where the algorithm actively selects specific samples from a large pool of unlabeled data to be labeled by an expert.
2. Purpose: Continual learning focuses on adapting to new information and updating existing models to incorporate new knowledge. It aims to avoid catastrophic forgetting and retain previously learned information while incorporating new data. Active learning, however, aims to reduce labeling effort by selecting the most informative samples for labeling, thereby improving model performance with fewer labeled instances.
3. Process: Continual learning involves training models on sequential or incremental data, where it adapts its parameters based on new examples without forgetting previously learned tasks or concepts. Active learning involves iteratively selecting a subset of unlabeled instances based on their potential for improving the model's performance and requesting labels for those instances.
4. Feedback loop: In continual learning, the feedback loop is based on comparing model predictions with ground truth labels and updating the model iteratively based on errors and new information. In active learning, the feedback loop involves querying an expert for labels on selected instances and incorporating those labels into training data.
5. Data requirements: Continual learning typically requires access to a continuous stream of data over time that covers various tasks or concepts. Active learning can work with static datasets but is particularly useful when dealing with large pools of unlabeled data or when labeling is expensive.
6. Challenges: Continual learning faces challenges such as catastrophic forgetting (forgetting previously learned knowledge), scalability as the amount of data grows over time, and preserving model performance across tasks. Active learning faces challenges in terms of sample selection strategies that balance exploration vs. exploitation and selecting representative samples for labeling.

In summary, continual learning focuses on adapting models over time while retaining previous knowledge, whereas active learning focuses on selecting informative instances for labeling to improve model performance. Continual learning deals with sequential or incremental data, while active learning can work with static or large pools of unlabeled data.