Domain Adaptation

Domain adaptation in computer vision addresses the challenge of adapting machine learning models trained on one domain (source domain) to perform well on a different but related domain (target domain). This is particularly important when the labeled data in the target domain is limited or unavailable.

Methods and Techniques

• Transfer Learning - Utilizes knowledge gained from the source domain to improve the learning process in the target domain, leveraging pre-trained models and features.

• Feature-level Adaptation - Aligns the feature distributions between the source and target domains, reducing the discrepancy in feature spaces.

• Domain-Invariant Representations - Encourages the model to learn representations that are insensitive to domain shifts, improving generalization to the target domain.

• Cycle-Consistent Generative Adversarial Networks (CycleGAN) - Transforms images from the source domain to the target domain and vice versa, aiming to learn domain-invariant representations.

• Batch Normalization Statistics Alignment - Normalizes the statistics (mean and variance) of feature maps between the source and target domains, reducing domain discrepancies.

• Unsupervised Domain Adaptation (UDA) - Adapts models using unlabeled target domain data, exploiting the unlabeled information to improve performance.