Adversarial Discriminative Domain Adaptation (ADDA) is a novel approach to domain adaptation that combines adversarial learning with discriminative feature learning. The method aims to improve the performance of deep networks in cross-domain tasks by learning a discriminative representation that can generalize across different domains. ADDA is designed to handle larger domain shifts compared to prior discriminative approaches, while avoiding the limitations of generative methods that are constrained to smaller shifts. It introduces a GAN-based loss function and allows for untied weight sharing, making it more flexible and effective. ADDA is based on a generalized framework for adversarial domain adaptation that subsumes previous state-of-the-art approaches as special cases. The framework allows for the comparison and understanding of different domain adaptation methods by considering factors such as weight-sharing, base models, and adversarial losses. The method first learns a discriminative representation using the source domain labels and then maps the target data to the same space using an asymmetric mapping learned through a domain-adversarial loss. This approach is simple yet powerful and achieves state-of-the-art results on standard cross-domain digit classification tasks and a new cross-modality object classification task. The method is evaluated on four different domain shifts, including three digit datasets (MNIST, USPS, SVHN) and a cross-modality task between RGB and depth images from the NYU depth dataset. ADDA outperforms existing methods on these tasks, demonstrating its effectiveness in adapting to different domains. The results show that ADDA can learn useful representations without requiring labeled data in the target domain, and it achieves significant improvements in classification accuracy across various classes. The method is also shown to be effective in handling domain shifts, as it can learn a representation that is more suitable for recognition in the target domain. Overall, ADDA provides a promising approach to domain adaptation by combining adversarial learning with discriminative feature learning.Adversarial Discriminative Domain Adaptation (ADDA) is a novel approach to domain adaptation that combines adversarial learning with discriminative feature learning. The method aims to improve the performance of deep networks in cross-domain tasks by learning a discriminative representation that can generalize across different domains. ADDA is designed to handle larger domain shifts compared to prior discriminative approaches, while avoiding the limitations of generative methods that are constrained to smaller shifts. It introduces a GAN-based loss function and allows for untied weight sharing, making it more flexible and effective. ADDA is based on a generalized framework for adversarial domain adaptation that subsumes previous state-of-the-art approaches as special cases. The framework allows for the comparison and understanding of different domain adaptation methods by considering factors such as weight-sharing, base models, and adversarial losses. The method first learns a discriminative representation using the source domain labels and then maps the target data to the same space using an asymmetric mapping learned through a domain-adversarial loss. This approach is simple yet powerful and achieves state-of-the-art results on standard cross-domain digit classification tasks and a new cross-modality object classification task. The method is evaluated on four different domain shifts, including three digit datasets (MNIST, USPS, SVHN) and a cross-modality task between RGB and depth images from the NYU depth dataset. ADDA outperforms existing methods on these tasks, demonstrating its effectiveness in adapting to different domains. The results show that ADDA can learn useful representations without requiring labeled data in the target domain, and it achieves significant improvements in classification accuracy across various classes. The method is also shown to be effective in handling domain shifts, as it can learn a representation that is more suitable for recognition in the target domain. Overall, ADDA provides a promising approach to domain adaptation by combining adversarial learning with discriminative feature learning.