# An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision
## 2 Domain Adaptation Techniques
### Evaluation of Domain Adaptation Techniques
\begin{tabular}{c c c c c c c c} \hline \hline
[31] & & & & by introducing a & & & & \\
[32] & & & & self-attention mechanism & & & & \\ \hline DANN-SA-MMD- & & & & & & & \\ IRM-RevGrad-CTC [32] & Adversarial & 92.60\% & Text classification & Enhanced MADA-IRM by & Better performance & More complex than & Improved \\
[33] & &  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Stack/Technology** & **Accuracy** & **Domain** & **Contribution** & **Advantages** & **Disadvantages** & **Uniqueness** \\ \hline MADA-MTL [139] & Adversarial & 91.70\% & Image classification & Enhanced DANN by introducing a multi-task learning framework & Better performance than DANN & More complex than DANN & Improved performance over \\ \hline MADA-RevGrad [140] & Adversarial & 91.30\% & Image classification & Enhanced MADA by introducing a reversal gradient technique & Better performance than MADA & More complex than DANN & Improved performance over \\ \hline MADA-SA-IRM-RevGrad [141] & Adversarial & 91.70\% & Image classification & Enhanced DANN-SA-IRM-based learning framework & Better performance than DANN-SA-IRM-based learning framework & More complex than DANN-SA-MMD-based learning framework & Improved performance over \\ \hline MADA-SA-AMMD- [142] & Adversarial & 92.70\% & Text classification & Enhanced DANN-SA-IM-based learning framework & Better performance than DANN-SA-AMMD-based learning framework & More complex than DANN-SA-MRM-based learning framework & Improved performance over \\ \hline MADA-SA-AMMD- [142] & Adversarial & 92.70\% & Text classification & Enhanced DANN-SA-IM-based learning framework & Better performance than DANN-SA-AMMD-based learning framework & More complex than DANN-SA-MRM-based learning framework & Improved performance over \\ \hline MADD [143] & Reconstruction & 89.40\% & Image classification & Enhanced MCD by introducing a domain adaptation loss & Better performance than MCD & More complex than DANN & Improved performance over \\ \hline MCD-DA [144] & Reconstruction & 92.50\% & Image classification & Enhanced MCD-DA by introducing an importance reweighting mechanism & Better performance performance than MCD-DA & More complex than MCD-DA & Improved performance over \\ \hline MCD-DA-RevGrad [146] & Reconstruction & 92.70\% & Image classification & Enhanced MCD-DA by introducing a reversal gradient technique & Better performance than MCD-DA & More complex than DANN & Improved performance over \\ \hline MDD [147] & Reconstruction & 88.20\% & Image classification & Introduced a multi-domain discriminator to DANN & Better performance on multiple domains & More complex than DANN & Improved performance on multiple domains \\ \hline \hline \end{tabular}
\end{table}
Table 8: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Stack/Technology** & **Accuracy** & **Domain** & **Continuation** & **Advantages** & **Disadvantages** & **Uniqueness** \\ \hline MMD-DA [148] & Reconstruction & 91.90\% & Image classification & Enhanced MMD by introducing a domain adaptation loss & Better performance than MMD & More complex than MMD & Improved performance over MMD \\ \hline MMD-DA-IRM [149] & Reconstruction & 92.00\% & Image classification & Enhanced MMD-DA by introducing an importance reweighing mechanism & Better performance than MMD-DA & More complex than MMD-DA & Improved performance over MMD-DA \\ \hline MMD-DA-RevGrad [150] & Reconstruction & 92.10\% & Image classification & Enhanced MMD-DA by introducing a reversal gradient technique & Better performance than MMD-DA & More complex than MMD-DA & Improved performance over MMD-DA \\ \hline MUNIT [151] & Generative & 89.70\% & Image translation & Improved a multi-modal image-to-image translation & Better performance than CycleGAN & More complex than CycleGAN & Improved performance over CycleGAN \\ \hline MUNIT-IRM [152] & Adversarial & 91.90\% & Image classification & Enhanced MADA-SA by reweighting an importance reweighing mechanism, a reversal gradient technique, and a self-attention mechanism & More complex than MMDA-SA & Improved performance over MMD-SA \\ \hline MUNIT-SA-IRM [153] & Generative & 92.30\% & Image translation & Enhanced MUNIT by introducing an importance reweighing mechanism & Better performance than MUNIT & More complex than MUNIT & Improved performance over MUNIT \\ \hline SimGAN [154] & Generative & 89.10\% & Image classification & Introduced a generative adversarial network to domain adaptation & Better performance than MUNIT & More complex than MUNIT & Improved performance over MMDAN \\ \hline SimGAN-IRM [155] & Generative & 91.70\% & Image classification & Enhanced SimGAN by introducing an importance reweighting mechanism & Better performance more complex than SimGAN & More complex than MUNIT & Improved performance over MMDAN \\ \hline \hline \end{tabular}
\end{table}
Table 8: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Stack/Technology** & **Accuracy** & **Domain** & **Contribution** & **Advantages** & **Disadvantages** & **Uniqueness** \\ \hline SimGAN-RevGrad [156] & Generative & 91.80\% & Image classification & Enhanced SimGAN by introducing a reversal gradient technique & Better performance than SimGAN & More complex than SimGAN & Improved performance over SimGAN \\ \hline SimGAN-8A-IBM [157] & Adversarial & 92.10\% & Image classification & Enhanced CDAN-SA- & Better performance than CDAN-SA- & More complex than performance over MD-IBM-RevGrad & \begin{tabular}{c} \(\text{CDAN-SA-MDD}\) \\ 1 BM-RevGrad \\ \end{tabular} \\ \hline StarGAN [158] & Generative & 90.00\% & Image translation & Introduced a starGAN for multi-domain image-to-image translation & Better performance than CycleGAN and & More complex than & \
\begin{tabular}{c} Improved \\ \(\text{CDAE}\) and \\ \(\text{MUNIT}\) on multiple domains \\ \end{tabular} \\ \hline TADAM [159] & Reconstruction & 88.60\% & Image classification & Introduced a target adaptation discriminator to DANN & Better performance on small datasets & More complex than & Improved performance on small datasets \\ \hline UNIT [16] & Generative & 89.90\% & Image translation & Introduced a unified framework for image-to-image translation & Better performance than CycleGAN and & More complex than CycleGAN and &
\begin{tabular}{c} Improved \\ \(\text{CDAE}\)and \\ \(\text{MUNIT}\) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 8: _Cont._Table 9 gives a summary of various publications on domain adaptation, emphasizing their merits and contributions. These papers offer a variety of strategies for enhancing model efficacy in domain adaptation challenges. Contributions include the introduction of adversarial strategies, generative models, multi-task learning, and other combinations of these techniques. Benefits include the better handling of complex interactions, realistic image generation, adaptability to new domains, and better use of unlabeled data.  
Table 9 presents a comprehensive overview of research sources focused on domain adaptation, offering several noteworthy conclusions and insights. One notable finding is the diverse range of innovative techniques introduced by researchers, including adversarial learning, generative adversarial networks, meta-learning, and self-supervised learning. These approaches exhibit significant promise in enhancing performance across various tasks and domains, as exemplified by [161], which introduces a multi-task learning framework for domain adaptation. Furthermore, certain models, such as [162], illustrate their potential by generating realistic target domain images, while [163] highlights the importance of learning invariant features for effective adaptation. Additionally, combining different techniques, as demonstrated in [164], with ensemble learning and self-supervised learning, can further contribute to improved domain adaptation performance. The table shows the variety of research projects, from object detection in industrial automation to semantic segmentation in autonomous cars [165]. The studies cover a wide range of topics, demonstrating the pervasive interest in and importance of domain adaptation in several practical applications [166].  
Overall, this table underscores the diversity of strategies and models developed by researchers to address domain adaptation challenges, providing valuable insights into the field's advancements and potential avenues for future research.  
\begin{table}
\begin{tabular}{c c c} \hline
**Paper** & **Advantages** & **Advantages** \\ \hline
[161] & Introduces a new multi-task learning framework for domain adaptation & Can improve performance on multiple tasks \\ \hline
[162] & Presents a model that works upon adversarial conditional image synthesis & Can improve performance by generating realistic images from the target domain \\ \hline
[167] & Presents a model that works upon adversarial conditional variational autoencoders & Can improve performance by using adversarial conditional variational autoencoders \\  & & to learn representations that are invariant to domain shift \\ \hline
[163] & Introduces a new adversarial training framework for domain adaptation that uses an ensemble of discriminators & Can improve performance by combining multiple discriminators \\ \hline
[164] & Presents a model that works upon adversarial training and meta-learning & Can improve performance by using adversarial training and meta-learning to learn a model that can generalize to new domains \\ \hline
[165] & Introduces a new method for domain adaptation that combines adversarial training and self-supervised learning & Can improve performance by learning features that are invariant to domain shift and self-supervised learning to learn representations that are transferable to new domains \\ \hline
[166] & Introduces a new conditional generative adversarial network framework for domain adaptation & Can improve performance by using conditional generative models \\ \hline
[168] & Presents a model that works upon conditional generative adversarial networks & Can improve performance by using conditional generative adversarial networks to generate realistic images from the target domain \\ \hline
[169] & Presents a model that works upon conditional Wasserstein generative adversarial networks & Can improve performance by generating realistic images from the target domain \\ \hline
[170] & Presents a model that works upon domain-invariant feature extraction & Can improve performance by using domain-invariant feature extraction to learn features that are invariant to domain shift \\ \hline
[171] & Introduces a new method for domain adaptation that combines ensemble learning and self-supervised learning & Can improve performance by combining multiple models and using self-supervised learning to learn learning to learn features that are transferable to new domains \\ \hline
[172] & Presents a model that works upon ensemble learning and transfer learning & Can improve performance by using ensemble learning and transfer learning to learn a model that can generalize to new domains \\ \hline
[173] & Presents a model that works upon few-shot learning and meta-learning & Can improve performance by using few-shot learning to learn a model that can generalize to new domains \\ \hline
[174] & Presents a model that works upon few-shot learning and meta-learning & Can improve performance by using few-shot learning and meta-learning to learn a model that can generalize to new domains \\ \hline \end{tabular}
\end{table}
Table 9: Systematic comparison of the literature published on the use of domain adaptation in the field of computer and robotic vision.  
\begin{table}
\begin{tabular}{c c c} \hline
**Paper** & **Contribution** & **Advantages** \\ \hline
[175] & \
\begin{tabular}{c} **Presents a model that works upon generative adversarial networks for** \\ semi-supervised learning** \\ \end{tabular} &
\begin{tabular}{c} **Can improve performance by learning from both labeled and unlabeled data** \\ \end{tabular} \\ \hline
[176] & \
\begin{tabular}{c} **Introduces a new method for learning invariant features for domain adaptation** \\ \end{tabular} &
\begin{tabular}{c} **Can improve performance by learning representations that are invariant to** \\ \end{tabular} \\ \hline
[177] & \
\begin{tabular}{c} **Introduces a new method for domain adaptation that combines invariant** \\ \end{tabular} &
\begin{tabular}{c} **Can improve performance by learning representations that are invariant to** \\ \end{tabular} \\ \hline
[177] & \
\begin{tabular}{c} **Introduces a new method for learning invariant features for domain adaptation that combines invariant** \\ \end{tabular} &
\begin{tabular}{c} **Can improve performance by learning representations that are invariant to** \\ \end{tabular} \\ \hline
[177] & \
\begin{tabular}{c} **Introduces a new method for domain adaptation that combines invariant** \\ \end{tabular} &
\begin{tabular}{c} **Can improve performance by learning representations that are transferable to** \\ \end{tabular} \\ \hline
[177] & \
\begin{tabular}{c} **Presents a model that works upon meta-learning for transferable features** \\ \end{tabular} &
\begin{tabular}{c} **Can improve performance by learning features that are transferable to new domains** \\ \end{tabular} \\ \hline
[178] & \
\begin{tabular}{c} **Presents a model that works upon multi-task learning and attention** \\ \end{tabular} &
\begin{tabular}{c} **Can improve performance by using multi-task learning and attention to learn** \\ \end{tabular} \\ \hline
[179] & \
\begin{tabular}{c} **Presents a model that works upon multi-task learning for few-shot** \\ \end{tabular} &
\begin{tabular}{c} **Can improve performance by learning multiple tasks with few examples** \\ \end{tabular} \\ \hline
[180] & \
\begin{tabular}{c} **Presents a model that works upon patch-level self-supervised learning** \\ \end{tabular} &
\begin{tabular}{c} **Can improve performance by using patch-level self-supervised learning to learn** \\ \end{tabular} \\ \hline
[181] & \
\begin{tabular}{c} **Presents a model that works upon self-supervised learning** \\ \end{tabular} &
\begin{tabular}{c} **Can improve performance by learning representations that are invariant to** \\ \end{tabular} \\ \hline
[182] & \
\begin{tabular}{c} **Presents a model that works upon self-supervised contrastive learning** \\ \end{tabular} &
\begin{tabular}{c} **Can improve performance by learning representations that are invariant to** \\ \end{tabular} \\ \hline
[183] & \
\begin{tabular}{c} **Presents a model that works upon self-supervised learning and synthetic data** \\ \end{tabular} &
\begin{tabular}{c} **Can improve performance by using self-supervised learning to learn features that are** \\ \end{tabular} \\ \hline
[184] & \
\begin{tabular}{c} **Presents a model that works upon synthetic data and domain-invariant** \\ \end{tabular} &
\begin{tabular}{c} **Can improve performance by learning synthetic data that are like the target** \\ \end{tabular} \\ \hline
[185] & \
\begin{tabular}{c} **Presents a model that works upon synthetic data and self-supervised learning** \\ \end{tabular} &
\begin{tabular}{c} **Can improve performance by using synthetic data and self-supervised learning to** \\ \end{tabular} \\ \hline
[186] & \
\begin{tabular}{c} **Presents a model that works upon synthetic data and self-supervised learning** \\ \end{tabular} &
\begin{tabular}{c} **Can improve performance by using synthetic data and self-supervised learning to** \\ \end{tabular} \\ \hline
[187] & \
\begin{tabular}{c} **Introduces a new Wasserstein adversarial training framework for domain adaptation** \\ \end{tabular} &
\begin{tabular}{c} **Can improve performance by using Wasserstein distance** \\ \end{tabular} \\ \hline
[188] & \
\begin{tabular}{c} **Presents a model that works upon adversarial learning and meta-learning** \\ \end{tabular} &
\begin{tabular}{c} **Can improve performance by using adversarial learning and meta-learning to learn a** \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 9: _Cont._
\begin{table}
\begin{tabular}{c c c} \hline
**Paper** & **Contribution** & **Advantages** \\ \hline
[189] & \
\begin{tabular}{c} _Presents a model that works upon adversarial learning and meta-learning_ \\ \end{tabular} &
\begin{tabular}{c} Can improve performance by using adversarial learning and meta-learning to learn a model that can generalize to new domains \\ \end{tabular} \\ \hline
[190] & \
\begin{tabular}{c} _Presents a model that works upon adversarial learning and self-supervised learning_ \\ \end{tabular} &
\begin{tabular}{c} Can improve performance by using adversarial learning and self-supervised learning \\ to learn a model that can generalize to new domains \\ \end{tabular} \\ \hline
[191] & \
\begin{tabular}{c} _Presents a model that works upon adversarial learning and transfer learning_ \\ \end{tabular} &
\begin{tabular}{c} Can improve performance by using adversarial learning and transfer learning to \\ learn a model that can generalize to new domains \\ \end{tabular} \\ \hline
[192] & \
\begin{tabular}{c} _Presents a model that works upon adversarial multi-agent reinforcement learning_ \\ \end{tabular} &
\begin{tabular}{c} Can improve performance by using adversarial multi-agent reinforcement learning \\ to learn a model that can generalize to new domains \\ \end{tabular} \\ \hline
[193] & \
\begin{tabular}{c} _Presents a model that works upon adversarial training and distillation_ \\ \end{tabular} &
\begin{tabular}{c} Can improve performance by using adversarial training and distillation to learn a model that can generalize to new domains \\ \end{tabular} \\ \hline
[194] & \
\begin{tabular}{c} _Presents a model that works upon adversarial training and self-supervised learning_ \\ \end{tabular} &
\begin{tabular}{c} Can improve performance by using adversarial training and self-supervised learning \\ to learn a model that can generalize to new domains \\ \end{tabular} \\ \hline
[195] & \
\begin{tabular}{c} _Presents a model that works upon attention and normalization_ \\ \end{tabular} &
\begin{tabular}{c} Can improve performance by using attention and normalization to learn \\ representations that are invariant to domain shift \\ \end{tabular} \\ \hline
[196] & \
\begin{tabular}{c} _Presents a model that works upon conditional generative adversarial networks and_ \\ CycleGAN \\ \end{tabular} &
\begin{tabular}{c} Can improve performance by using conditional generative adversarial networks and \\ CycleGAN to generate realistic images from the target domain \\ \end{tabular} \\ \hline
[197] & \
\begin{tabular}{c} _Presents a model that works upon ensemble learning and domain adaptation_ \\ \end{tabular} &
\begin{tabular}{c} Can improve performance by using ensemble learning and domain adaptation to \\ learn a model that can generalize to new domains \\ \end{tabular} \\ \hline
[198] & \
\begin{tabular}{c} _Presents a model that works upon few-shot learning and data augmentation_ \\ \end{tabular} &
\begin{tabular}{c} Can improve performance by using few-shot learning and data augmentation to \\ learn a model that can generalize to new domains \\ \end{tabular} \\ \hline
[199] & \
\begin{tabular}{c} _Presents a model that works upon few-shot learning and interpolation_ \\ \end{tabular} &
\begin{tabular}{c} Can improve performance by using few-shot learning and self-supervised learning \\ to learn a model that can generalize to new domains \\ \end{tabular} \\ \hline
[200] & \
\begin{tabular}{c} _Presents a model that works upon few-shot learning and self-supervised learning_ \\ \end{tabular} &
\begin{tabular}{c} Can improve performance by using few-shot learning and self-supervised learning \\ to learn a model that can generalize to new domains \\ \end{tabular} \\ \hline
[201] & \
\begin{tabular}{c} _Presents a model that works upon generative adversarial networks and \\ self-supervised learning_ \\ \end{tabular} &
\begin{tabular}{c} Can improve performance by using generative adversarial networks and \\ self-supervised learning to learn features that are invariant to domain shift \\ \end{tabular} \\ \hline
[202] & \
\begin{tabular}{c} _Presents a model that works upon graph neural networks_ \\ \end{tabular} &
\begin{tabular}{c} Can improve performance by using graph neural networks to learn representations \\ that are invariant to domain shift \\ \end{tabular} \\ \hline
[203] & \
\begin{tabular}{c} _Presents a model that works upon invariant feature extraction and distillation_ \\ \end{tabular} &
\begin{tabular}{c} Can improve performance by using invariant feature extraction and distillation to \\ learn a model that can generalize to new domains \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 9: _Cont._
\begin{table}
\begin{tabular}{c c c} \hline
**Paper** & **Contribution** & **Advantages** \\ \hline
[204] & \
\begin{tabular}{c} _Presents a model that works upon invariant feature extraction and interpolation_ \\ \end{tabular} &
\begin{tabular}{c} Can improve performance by using invariant feature extraction and interpolation to \\ learn features that are invariant to domain shift \\ \end{tabular} \\ \hline
[205] & \
\begin{tabular}{c} _Presents a model that works upon meta-learning and importance reweighting_ \\ \end{tabular} &
\begin{tabular}{c} Can improve performance by using meta-learning and importance reweighting to \\ learn a model that can generalize to new domains \\ \end{tabular} \\ \hline
[206] &
\begin{tabular}{c} _Presents a model that works upon multi-task learning and_ \\ cross-domain augmentation to learn a model that can generalize to new domains \\ \end{tabular} \\ \hline
[207] & \
\begin{tabular}{c} _Presents a model that works upon multi-task learning and domain-invariant feature extraction_ \\ \end{tabular} &
\begin{tabular}{c} Can improve performance by using multi-task learning and domain-invariant \\ feature extraction to learn features that are invariant to domain shift \\ \end{tabular} \\ \hline
[208] &
\begin{tabular}{c} _Presents a model that works upon self-attention and conditional generative_ \\ adversarial networks to learn representations that are invariant to domain shift \\ \end{tabular} \\ \hline
[209] & \
\begin{tabular}{c} _Presents a model that works upon self-supervised contrastive learning_ \\ \end{tabular} &
\begin{tabular}{c} Can improve performance by using self-supervised contrastive learning to learn \\ features that are invariant to domain shift \\ \end{tabular} \\ \hline
[210] & \
\begin{tabular}{c} _Presents a model that works upon self-supervised contrastive learning_ \\ \end{tabular} &
\begin{tabular}{c} Can improve performance by using self-supervised contrastive learning to learn \\ representations that are invariant to domain shift \\ \end{tabular} \\ \hline
[211] & \
\begin{tabular}{c} _Presents a model that works upon self-supervised learning and transfer learning_ \\ \end{tabular} &
\begin{tabular}{c} Can improve performance by using self-supervised learning and transfer learning to \\ learn a model that can generalize to new domains \\ \end{tabular} \\ \hline
[212] & \
\begin{tabular}{c} _Presents a model that works upon synthetic data and transfer learning_ \\ \end{tabular} &
\begin{tabular}{c} Can improve performance by using synthetic data and transfer learning to \\ features that are invariant to domain shift \\ \end{tabular} \\ \hline
[213] & \
\begin{tabular}{c} _Presents a model that works upon temporal ensembling_ \\ \end{tabular} &
\begin{tabular}{c} Can improve performance by using temporal ensembling to learn a model that can generalize to new domains \\ \end{tabular} \\ \hline
[214] & \
\begin{tabular}{c} _Presents a model that works upon Wasserstein autoencoders_ \\ \end{tabular} &
\begin{tabular}{c} Can improve performance by using Wasserstein autoencoders to learn \\ representations that are invariant to domain shift \\ \end{tabular} \\ \hline
[215] & \
\begin{tabular}{c} _Presents a model that works upon data augmentation and self-supervised learning_ \\ \end{tabular} &
\begin{tabular}{c} Can improve performance by using data augmentation and self-supervised learning to \\ to learn features that are invariant to domain shift \\ \end{tabular} \\ \hline
[216] & \
\begin{tabular}{c} _Presents a model that works upon adversarial learning and contrastive learning_ \\ \end{tabular} &
\begin{tabular}{c} Can improve performance by using adversarial learning and contrastive learning to \\ learn features that are invariant to domain shift \\ \end{tabular} \\ \hline
[217] & \
\begin{tabular}{c} _Presents a model that works upon invariant feature extraction and distillation_ \\ \end{tabular} &
\begin{tabular}{c} Can improve performance by using invariant feature extraction and distillation to \\ learn features that are invariant to domain shift \\ \end{tabular} \\ \hline
[218] & \
\begin{tabular}{c} _Presents a model that works upon semi-supervised learning and transfer learning_ \\ \end{tabular} &
\begin{tabular}{c} Can improve performance by using semi-supervised learning and transfer learning \\ to learn a model that can generalize to new domains \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 9: _Cont._
\begin{table}
\begin{tabular}{c c c} \hline
**Paper** & **Contribution** & **Advantages** \\ \hline
[219] &
\begin{tabular}{c} **Presents a model that works upon few-shot learning and invariant feature extraction to learn features that are invariant to domain shift \\ \end{tabular} \\ \hline
[220] & \
\begin{tabular}{c} **Presents a model that works upon adversarial learning and meta-learning** \\ \end{tabular} &
\begin{tabular}{c} **Can improve performance by using adversarial learning and meta-learning to learn a model that can generalize to new domains** \\ \end{tabular} \\ \hline
[221] & \
\begin{tabular}{c} **Presents a model that works upon self-supervised learning and generative** \\ \end{tabular} &
\begin{tabular}{c} **Can improve performance by using self-supervised learning and generative** \\ \end{tabular} \\ \hline
[222] & \
\begin{tabular}{c} **Presents a model that works upon adversarial learning and transfer learning** \\ \end{tabular} &
\begin{tabular}{c} **Can improve performance by using adversarial learning and transfer learning to** \\ \end{tabular} \\ \hline
[223] & \
\begin{tabular}{c} **Presents a model that works upon ensemble learning and meta-learning** \\ \end{tabular} &
\begin{tabular}{c} **Can improve performance by using ensemble learning and meta-learning to learn a model that can generalize to new domains** \\ \end{tabular} \\ \hline
[224] & \
\begin{tabular}{c} **Presents a model that works upon adversarial learning and invariant representations** \\ \end{tabular} &
\begin{tabular}{c} **Can improve performance by using adversarial learning and invariant** \\ \end{tabular} \\ \hline
[225] & \
\begin{tabular}{c} **Presents a model that works upon attention and self-supervised learning** \\ \end{tabular} &
\begin{tabular}{c} **Can improve performance by using attentional and self-supervised learning to learn** \\ \end{tabular} \\ \hline
[226] & \
\begin{tabular}{c} **Presents a model that works upon conditional domain adversarial networks and** \\ \end{tabular} &
\begin{tabular}{c} **Can improve performance by using conditional domain adversarial networks and** \\ \end{tabular} \\ \hline
[227] & \
\begin{tabular}{c} **Presents a model that works upon meta-learning and transfer learning** \\ \end{tabular} &
\begin{tabular}{c} **Can improve performance by using meta-learning and transfer learning to learn a model that can generalize to new domains** \\ \end{tabular} \\ \hline
[228] & \
\begin{tabular}{c} **Presents a model that works upon few-shot learning and adversarial learning** \\ \end{tabular} &
\begin{tabular}{c} **Can improve performance by using few-shot learning and adversarial learning to** \\ \end{tabular} \\ \hline
[229] & \
\begin{tabular}{c} **Presents a model that works upon synthetic data and meta-learning** \\ \end{tabular} &
\begin{tabular}{c} **Can improve performance by using synthetic data and meta-learning to learn a model that can generalize to new domains** \\ \end{tabular} \\ \hline
[230] & \
\begin{tabular}{c} **Presents a model that works upon adversarial learning and multi-task learning** \\ \end{tabular} &
\begin{tabular}{c} **Can improve performance by using adversarial learning and multi-task learning to** \\ \end{tabular} \\ \hline
[231] & \
\begin{tabular}{c} **Presents a model that works upon conditional generative adversarial networks and** \\ \end{tabular} &
\begin{tabular}{c} **Can improve performance by using conditional generative adversarial networks and** \\ \end{tabular} \\ \hline
[232] & \
\begin{tabular}{c} **Presents a model that works upon ensemble learning and self-supervised learning** \\ \end{tabular} &
\begin{tabular}{c} **Can improve performance by using ensemble learning and self-supervised learning** \\ \end{tabular} \\ \hline
[233] & \
\begin{tabular}{c} **Presents a model that works upon ensemble learning and self-supervised learning** \\ \end{tabular} &
\begin{tabular}{c} **Can improve performance by using ensemble learning and self-supervised learning** \\ \end{tabular} \\ \hline
[234] & \
\begin{tabular}{c} **Introduces a new method for domain adaptation that learns multi-domain** \\ \end{tabular} &
\begin{tabular}{c} **Can improve performance by learning representations that are invariant to** \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 9: _Cont._
\begin{table}
\begin{tabular}{c c c} \hline
**Paper** & **Contribution** & **Advantages** \\ \hline
[234] & \
\begin{tabular}{c} _Presents a model that works upon self-supervised learning and conditional_ \\ adversarial networks \\ \end{tabular} &
\begin{tabular}{c} _Can improve performance by using self-supervised learning and conditional_ \\ adversarial networks to learn features that are invariant to domain shift_ \\ \end{tabular} \\ \hline
[235] & \
\begin{tabular}{c} _Presents a model that works upon adversarial learning and few-shot learning_ \\ \end{tabular} &
\begin{tabular}{c} _Can improve performance by using adversarial learning and few-shot learning to_ \\ learn a model that can generalize to new domains_ \\ \end{tabular} \\ \hline
[236] &
\begin{tabular}{c} _Presents a model that works upon domain-invariant feature aggregation and_ \\ self-supervised learning to learn features that are invariant to domain shift_ \\ \end{tabular} \\ \hline
[237] & \
\begin{tabular}{c} _Presents a model that works upon generative adversarial networks and_ \\ meta-learning \\ \end{tabular} &
\begin{tabular}{c} _Can improve performance by using generative adversarial networks and_ \\ meta-learning to learn features that are invariant to domain shift_ \\ \end{tabular} \\ \hline
[238] & \
\begin{tabular}{c} _Presents a model that works upon adversarial learning and synthetic data_ \\ \end{tabular} &
\begin{tabular}{c} _Can improve performance by using adversarial learning and synthetic data to learn features that are invariant to domain shift_ \\ \end{tabular} \\ \hline
[239] & \
\begin{tabular}{c} _Presents a model that works upon multi-task learning and meta-learning_ \\ \end{tabular} &
\begin{tabular}{c} _Can improve performance by using multi-task learning and meta-learning to learn a model that can generalize to new domains_ \\ \end{tabular} \\ \hline
[240] & \
\begin{tabular}{c} _Presents a model that works upon ensemble learning and transfer learning_ \\ \end{tabular} &
\begin{tabular}{c} _Can improve performance by using ensemble learning and transfer learning to learn a model that can generalize to new domains_ \\ \end{tabular} \\ \hline
[241] & \
\begin{tabular}{c} _Presents a model that works upon adversarial learning and self-supervised learning_ \\ \end{tabular} &
\begin{tabular}{c} _Can improve performance by using adversarial learning and self-supervised learning_ \\ to learn a model that can generalize to new domains_ \\ \end{tabular} \\ \hline
[242] & \
\begin{tabular}{c} _Presents additional generative adversarial networks for domain adaptation_ \\ \end{tabular} &
\begin{tabular}{c} _Can improve performance by using adversarial learning and self-supervised learning_ \\ to learn a model that can generalize to new domains_ \\ \end{tabular} \\ \hline
[243] & \
\begin{tabular}{c} _Cumbes distillation and semi-supervised learning for domain adaptation_ \\ \end{tabular} &
\begin{tabular}{c} _Can improve performance by using both techniques_ \\ \end{tabular} \\ \hline
[244] & \
\begin{tabular}{c} _Uses graph neural networks for domain adaptation_ \\ \end{tabular} &
\begin{tabular}{c} _Can better handle complex relationships between features_ \\ \end{tabular} \\ \hline
[245] & \
\begin{tabular}{c} _Cumbes meta-learning and adversarial training for domain adaptation_ \\ \end{tabular} &
\begin{tabular}{c} _Can improve performance by learning to adapt to new domains_ \\ \end{tabular} \\ \hline
[246] & \
\begin{tabular}{c} _Cumbes multi-task learning and distillation for domain adaptation_ \\ \end{tabular} &
\begin{tabular}{c} _Can improve performance by using both techniques_ \\ \end{tabular} \\ \hline
[247] & \
\begin{tabular}{c} _Uses reinforcement learning for domain adaptation_ \\ \end{tabular} &
\begin{tabular}{c} _Can improve performance by learning to adapt to new domains_ \\ \end{tabular} \\ \hline
[248] & \
\begin{tabular}{c} _Uses self-supervised learning for domain adaptation_ \\ \end{tabular} &
\begin{tabular}{c} _Can improve performance by using unlabeled data from the target domain_ \\ \end{tabular} \\ \hline
[249] & \
\begin{tabular}{c} _Introduces a method for adversarial learning with Wasserstein distance for_ \\ domain adaptation_ \\ \end{tabular} &
\begin{tabular}{c} _Can improve performance by using a more robust distance metric_ \\ \end{tabular} \\ \hline
[250] & \
\begin{tabular}{c} _Introduces a co-training framework for domain adaptation_ \\ \end{tabular} &
\begin{tabular}{c} _Can improve performance by using multiple learners_ \\ \end{tabular} \\ \hline
[251] & \
\begin{tabular}{c} _Introduces a method for learning to correct feature distributions for_ \\ domain adaptation_ \\ \end{tabular} &
\begin{tabular}{c} _Can improve performance by explicitly modeling the domain shift_ \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 9: Cont.  
\begin{table}
\begin{tabular}{c c c} \hline
**Paper** & **Advantages** & **\\ \hline
[252] & Combines adversarial training and data augmentation for domain adaptation & Can improve performance by using both techniques \\ \hline
[253] & Introduces a new adversarial training framework for domain adaptation & Can improve performance by using virtual adversarial examples \\ \hline
[254] & Introduces a Bayesian deep learning framework for domain adaptation & Can better handle uncertainty in the data \\ \hline
[255] & Introduces a new conditional Wasserstein GAN framework for domain adaptation & Can improve performance by using conditional generative models \\ \hline
[256] &
\begin{tabular}{c} _Presents a model that works upon domain-invariant feature aggregation_ \\ \end{tabular} & Can improve performance by aggregating features from multiple domains \\ \hline
[257] &
\begin{tabular}{c} _Presents a model that works upon few-shot learning_ \\ \end{tabular} & Can improve performance by using few-shot learning to learn a model that can \\ \end{tabular} \\ \hline
[258] &
\begin{tabular}{c} _Introduces a generative adversarial network for domain adaptation for person_ \\ \end{tabular} & Can generate realistic images from the target domain \\ \hline
[259] &
\begin{tabular}{c} _Introduces a graph neural network for domain adaptation_ \\ \end{tabular} & Can better handle complex relationships between data points \\ \hline
[260] &
\begin{tabular}{c} _Introduces a hierarchical self-attention mechanism for domain adaptation_ \\ \end{tabular} & Can better handle complex relationships between features \\ \hline
[261] &
\begin{tabular}{c} _Introduces a new method for learning invariant representations for \\ domain adaptation_ \\ \end{tabular} & Can improve performance by learning representations that are invariant to \\ \hline
[262] &
\begin{tabular}{c} _Introduces a reinforcement learning framework for domain adaptation_ \\ \end{tabular} & Can learn to adapt to new domains in an online setting \\ \hline
[263] &
\begin{tabular}{c} _Presents a model that works upon self-supervised learning_ \\ \end{tabular} & Can improve performance by using self-supervised learning to learn features that are invariant to domain shift \\ \hline
[264] &
\begin{tabular}{c} _Presents a model that works upon synthetic data_ \\ \end{tabular} & Can improve performance by generating synthetic data that are like the \\ \hline
[265] &
\begin{tabular}{c} _Introduces a variational autoencoder for domain adaptation_ \\ \end{tabular} & Can generate realistic images from the target domain \\ \hline
[266] &
\begin{tabular}{c} _Introduces a Wasserstein distance for domain adaptation_ \\ \end{tabular} & Can better handle data with large domain shifts \\ \hline
[267] &
\begin{tabular}{c} _Introduces an ensemble domain adaptation framework_ \\ \end{tabular} & Can improve performance by combining multiple models \\ \hline
[268] &
\begin{tabular}{c} _Introduces a joint distribution adaptation framework for domain adaptation_ \\ \end{tabular} & Can improve performance by jointly adapting the source and target distributions \\ \hline
[269] &
\begin{tabular}{c} _Introduces a meta-learning framework for domain adaptation_ \\ \end{tabular} & Can improve performance by learning to adapt to new domains \\ \hline
[270] &
\begin{tabular}{c} _Introduces a transfer learning framework for domain adaptation_ \\ \end{tabular} & Can improve performance by using a pre-trained model \\ \hline
[271] &
\begin{tabular}{c} _Introduces a deep generative model for domain adaptation_ \\ \end{tabular} & Can generate realistic images from the target domain \\ \hline
[272] &
\begin{tabular}{c} _Introduces an importance reweighting mechanism for domain adaptation_ \\ \end{tabular} & Can better handle imbalanced datasets \\ \hline \end{tabular}