# Unsupervised Domain Adaptation with Coupled Generative Adversarial Autoencoders
## 4 Experiment Results and Evaluation
### Facial Expression Recognition  
We first evaluate our method on cross domain facial expression recognition task with three publicly available facial expression datasets: JAFFE, MMI and CK+. The images in these datasets have different resolutions and illuminations, and the subjects vary in gender, age and cultural background. Figure 3 shows some of the sample images from these datasets. **JAFFE** dataset [35; 36] contains 213 facial expression images. These images are from 10 Japanese females with seven expressions (angry, disgust, fear, happy, sad, surprise and neutral). We use all of the images in JAFFE in our experiments. **MMI** dataset [37; 38] consists of over 2900 videos as well as still images of 75 subjects, in which 235 videos have emotional labels. We choose the peak frame of each video that has the six basic emotions (angry, disgust, fear, happy, sad and surprise) and the first frame of these videos as neutral emotion images. In total we use 242 images from MMI. **CK+** dataset [39] consists of 593 image sequences from 123 subjects, 327 sequences of which have emotional labels. The dataset labels seven expressions including angry, disgust, fear, happy, sad, surprise, and contempt. We only choose the peak frame from the sequences labelled with the first six expressions. In addition, we choose the first frame from some of the sequences as neutral samples. In total we use 363 images from CK+. In this experiment, the network structure of our method is shown in Figure 2. Since the facial expression datasets are rather small, to avoid over-fitting, we use the Alexnet model pre-trained on ImageNet as the base model of the classifier and fine-tune it in our experiment. Table 1 shows experiment results of the classifier's accuracy tested on the target domain. In Table 1, the source model is trained with only the labeled source dataset. As for the adapted model, to evaluate the effectiveness of our proposed method, we train with three different settings. In all three settings, the parameters of the low-level layers in encoders, decoders and discriminators are not shared, which are the first layer of the encoder, the last layer of the decoder and the first layer of the discriminator. As for the high-level layers, the first experiment shares the weights of all of these layers, which is a similar structure to UNIT described in [19]. The second experiment has decoupled high-level layers in \(ENC\) and \(DEC\), which means we do not share the last layer of the two encoders and the first layer of the two decoders. The last one is to have decoupled high-level layers in \(ENC\), \(DEC\) as well as \(D\), which is the same setting  
Figure 3: Sample images from facial expression recognition datasets. described in Figure 2. A decoupled \(D\) means we do not share the last two layers in the discriminators. Figure 4 shows examples of the style-transferred images generated by UNIT and the last experiment setting of CGAA. In each domain adaptation, we use all available source examples and target examples and resize them to 224 \(\times\) 224 pixels to train the generative adversarial networks. Only the label information of the source dataset is used to train the classifier. Optimization is done on Pytorch using Adam with the learning rate set as 0.0001 and the weight decay as 0.0001. The \(\alpha\), \(\beta\) and \(\gamma\) in Equation (8) are set as 1.0, 1.0 and 0.1. Figure 4 shows some of the style-transferred images generated in the C\(\rightarrow\)J domain adaptation under our last kind of network setting. The experiment results in Table 1 show that our CGAA model with partially-decoupled high level layers outperforms the model with all the high level layers tied-up in all six domain adaptations. In addition, we find that decoupling the encoder-decoder can lead to a significant increase of recognition accuracy, whereas decoupling the discriminator has only a small impact on the experiment result. Therefore, in other experiments described in this paper, we use the last setting in Table 1 as CGAA for evaluation.