This paper introduces a novel unsupervised domain adaptation algorithm called Deep Reconstruction-Classification Network (DRCN) for visual object recognition. DRCN jointly learns a shared encoding representation for two tasks: supervised classification of labeled source data and unsupervised reconstruction of unlabeled target data. This approach not only preserves discriminability but also encodes useful information from the target domain. The model is optimized using backpropagation, similar to standard neural networks. Experimental results on various cross-domain object recognition tasks show that DRCN outperforms state-of-the-art algorithms by up to 8% in accuracy. Additionally, visual analysis reveals that DRCN transforms source images into appearances resembling the target dataset, suggesting that the learned representation effectively encodes both the structure of target images and the classification of source images. The paper also provides a formal analysis to justify the algorithm's objective in the context of domain adaptation.This paper introduces a novel unsupervised domain adaptation algorithm called Deep Reconstruction-Classification Network (DRCN) for visual object recognition. DRCN jointly learns a shared encoding representation for two tasks: supervised classification of labeled source data and unsupervised reconstruction of unlabeled target data. This approach not only preserves discriminability but also encodes useful information from the target domain. The model is optimized using backpropagation, similar to standard neural networks. Experimental results on various cross-domain object recognition tasks show that DRCN outperforms state-of-the-art algorithms by up to 8% in accuracy. Additionally, visual analysis reveals that DRCN transforms source images into appearances resembling the target dataset, suggesting that the learned representation effectively encodes both the structure of target images and the classification of source images. The paper also provides a formal analysis to justify the algorithm's objective in the context of domain adaptation.