# Unsupervised Domain Adaptation with Coupled Generative Adversarial Autoencoders
## 4 Experiment Results and Evaluation
### Office-Home Dataset  
Finally, we test our model on Office-home dataset [13]. This is a newer, larger and more challenging dataset compared to the classic Office dataset. It has about 15,500 images cross 4 domains, with each domain containing images from 65 classes of everyday objects. As shown in Figure 8, the four domains are: Art, Clipart, Product and Real-world. Images in Art are artistic depictions of objects and Clipart contains clipart images. Product consists of images of objects without background and Real-World consists of images of objects captured with a camera. We conduct this experiment with the same setting as the classic Office dataset experiment. Table 3 shows that our results outperform competitors in all of the domain adaptations in this experiment.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Training Method** & **A\(\rightarrow\)W** & **W\(\rightarrow\)A** & **W\(\rightarrow\)D** & **D\(\rightarrow\)W** & **A\(\rightarrow\)D** & **D\(\rightarrow\)A** \\ \hline Source & 0.670 & 0.498 & 0.952 & 0.941 & 0.689 & 0.515 \\ DDC & 0.594 & – & 0.917 & 0.925 & – & – \\ DAN & 0.685 & 0.531 & 0.990 & 0.960 & 0.670 & 0.540 \\ DAH & 0.683 & 0.530 & 0.988 & 0.961 & 0.665 & 0.555 \\ DRCN & 0.687 & 0.549 & 0.990 & 0.964 & 0.668 & 0.560 \\ DANN & 0.730 & – & 0.992 & 0.964 & – & – \\ RTN & 0.733 & 0.510 & **0.996** & 0.968 & 0.710 & 0.505 \\ ADDA & 0.751 & – & **0.996** & **0.970** & – & – \\ CoGAN & 0.745 & 0.549 & **0.996** & 0.968 & 0.710 & 0.560 \\ UNIT & 0.751 & 0.566 & 0.992 & 0.968 & 0.715 & 0.568 \\ \hline CGAA & **0.752** & **0.575** & **0.996** & 0.957 & **0.723** & **0.572** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Recognition accuracy evaluation for domain adaptation on the Office dataset. Amazon (A), webcam (W), dslr (D). A\(\rightarrow\)W indicates A is the source dataset and W is the target dataset. Bold numbers are the best results.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline
**Method** & **A\(\rightarrow\)C** & **A\(\rightarrow\)P** & **A\(\rightarrow\)R** & **C\(\rightarrow\)A** & **C\(\rightarrow\)P** & **C\(\rightarrow\)R** & **P\(\rightarrow\)A** & **P\(\rightarrow\)C** & **P\(\rightarrow\)R** & **R\(\rightarrow\)A** & **R\(\rightarrow\)C** & **R\(\rightarrow\)P** \\ \hline DAN [1] & 0.307 & 0.422 & 0.541 & 0.328 & 0.476 & 0.498 & 0.291 & 0.341 & 0.567 & 0.436 & 0.383 & 0.627 \\ DANN [1] & 0.333 & 0.430 & 0.544 & 0.322 & 0.491 & 0.498 & 0.305 & 0.381 & 0.568 & 0.447 & 0.427 & 0.647 \\ DAH & 0.316 & 0.408 & 0.517 & 0.347 & 0.519 & 0.528 & 0.299 & 0.396 & 0.607 & 0.450 & 0.451 & 0.625 \\ CoGAN & 0.399 & 0.545 & 0.672 & 0.471 & 0.570 & 0.579 & 0.478 & 0.406 & 0.635 & 0.580 & 0.489 & 0.728 \\ UNIT & 0.404 & 0.554 & 0.670 & 0.480 & 0.572 & 0.583 & 0.509 & 0.412 & 0.658 & 0.599 & 0.503 & 0.726 \\ \hline CGAA & **0.434** & **0.571** & **0.676** & **0.499** & **0.577** & **0.591** & **0.517** & **0.435** & **0.662** & **0.612** & **0.517** & **0.749** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Recognition accuracy evaluation for domain adaptation on the office-home dataset. Art (A), Clipart (C), Product (P), Real-World (R). A\(\rightarrow\)C indicates A is the source dataset and C is the target dataset. Bold numbers are the best results.  
Figure 7: Sample images from Office dataset.