This paper introduces a new approach for domain adaptation, where data at training and test time come from similar but different distributions. The method, called Domain-Adversarial Neural Networks (DANN), is inspired by domain adaptation theory, which suggests that effective domain transfer requires features that are discriminative for the source task but invariant to domain shifts. DANN trains neural networks on labeled source data and unlabeled target data, promoting features that are both discriminative for the source task and domain-invariant. This is achieved by adding a gradient reversal layer to the network, which allows the model to learn features that are indistinguishable to a domain classifier. The resulting architecture can be trained using standard backpropagation and stochastic gradient descent, making it easy to implement with existing deep learning frameworks. The approach is validated on two classification tasks—document sentiment analysis and image classification—where it achieves state-of-the-art performance on standard benchmarks. It is also applied to person re-identification, where domain-adversarial learning improves the performance of deep architectures. The method is theoretically grounded in the concept of H-divergence, which measures the difference between distributions. The paper also provides an experimental evaluation of DANN on various architectures and applications, demonstrating its effectiveness in domain adaptation tasks.This paper introduces a new approach for domain adaptation, where data at training and test time come from similar but different distributions. The method, called Domain-Adversarial Neural Networks (DANN), is inspired by domain adaptation theory, which suggests that effective domain transfer requires features that are discriminative for the source task but invariant to domain shifts. DANN trains neural networks on labeled source data and unlabeled target data, promoting features that are both discriminative for the source task and domain-invariant. This is achieved by adding a gradient reversal layer to the network, which allows the model to learn features that are indistinguishable to a domain classifier. The resulting architecture can be trained using standard backpropagation and stochastic gradient descent, making it easy to implement with existing deep learning frameworks. The approach is validated on two classification tasks—document sentiment analysis and image classification—where it achieves state-of-the-art performance on standard benchmarks. It is also applied to person re-identification, where domain-adversarial learning improves the performance of deep architectures. The method is theoretically grounded in the concept of H-divergence, which measures the difference between distributions. The paper also provides an experimental evaluation of DANN on various architectures and applications, demonstrating its effectiveness in domain adaptation tasks.