Supervised vs. Unsupervised: CNNs require labeled
data for training, making them suited for supervised learning tasks
where the model learns from explicit examples with known outcomes.
Autoencoders, in contrast, utilize unlabeled data and excel in
unsupervised learning, focusing on learning data representations and
detecting anomalies without predefined labels.
Output: CNNs produce predictions or classifications
based on input data labels (classification/regression), whereas
autoencoders aim to reconstruct input data or generate compressed
representations for further analysis.
Use Cases: CNNs are ideal for tasks involving
structured data such as detector images, where precise classification or
segmentation is needed. Autoencoders are particularly useful for
exploratory tasks, anomaly detection, and dimensionality reduction in
complex datasets where direct supervision is not available.
