## 4 Experimental Results
### Model Analysis
#### 4.6.1 Ablation Study  
In this ablation study, we evaluated the usefulness of each of the proposed methods. In Table 3, we show the results of the ablation experiments. Experiment (a) only adds a semantic decoder to the baseline for training, and the GZSL's performance, \(H\), is enhanced by 2.1%. Experiment (b) adds the semantic decoder's output to the visual reconstruction of the disentanglement module, and the GZSL's performance, \(H\), is enhanced by 2.1%. Experiment (c) involves training a classifier by joining the semantic-consistent features from the semantic decoder's output and the disentanglement encoder output; the GZSL performance, \(H\), is enhanced by 2.3%.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**AwA2**} & \multicolumn{3}{c}{**CUB**} & \multicolumn{3}{c}{**FLO**} & \multicolumn{3}{c}{**APY**} \\ \cline{2-13}  & \(\mathbf{U}\) & \(\mathbf{S}\) & \(\mathbf{H}\) & \(\mathbf{U}\) & \(\mathbf{S}\) & \(\mathbf{H}\) & \(\mathbf{U}\) & \(\mathbf{S}\) & \(\mathbf{H}\) & \(\mathbf{U}\) & \(\mathbf{S}\) & \(\mathbf{H}\) \\ \hline f-CLSWGAN [8] & 56.1 & 65.5 & 60.4 & 43.7 & 57.7 & 49.7 & 59 & 73.8 & 65.6 & 32.9 & 61.7 & 42.9 \\ CANZSL [36] & 49.7 & 70.2 & 58.2 & 47.9 & 58.1 & 52.5 & 58.2 & 77.6 & 66.5 & - & - & - \\ LisGAN [37] & 52.6 & 76.3 & 62.3 & 46.5 & 57.9 & 51.6 & 57.7 & 83.8 & 68.3 & 34.3 & 68.2 & 45.7 \\ CADA-VAE [17] & 55.8 & 75.0 & 63.9 & 51.6 & 53.5 & 52.4 & 51.6 & 75.6 & 61.3 & 31.7 & 55.1 & 40.3 \\ f-VAEGAN-D2 [18] & 57.6 & 70.6 & 63.5 & 48.4 & 60.1 & 53.6 & 56.8 & 74.9 & 64.6 & - & - & - \\ TF-VAEGAN [6] & 59.8 & 75.1 & 66.6 & 52.8 & 64.7 & 58.1 & 62.5 & 84.1 & 71.7 & - & - & - \\ TF-VAEGAN * [6] & 55.5 & 83.6 & 66.7 & 63.8 & 79.3 & 70.7 & 69.5 & 92.5 & 79.4 & - & - & - \\ FREE [7] & 60.4 & 75.4 & 67.1 & 55.7 & 59.9 & 57.7 & 67.4 & 84.5 & 75.0 & - & - & - \\ cycle-CLSWGAN [14] & - & - & - & 59.3 & 47.9 & 53.0 & 59.2 & 72.5 & 65.1 & - & - & - \\ CE-GZSL [20] & 63.1 & 78.6 & 70.0 & 63.9 & 66.8 & 65.3 & 69.0 & 78.7 & 73.5 & - & - & - \\ RGN [21] & 68.3 & 78.8 & 73.2 & 61.0 & 62.2 & 61.6 & 68.2 & 88.9 & 77.0 & - & - & - \\ SDGZSL [10] & 64.6 & 73.6 & 68.8 & 59.9 & 66.4 & 63.0 & 83.3 & 90.2 & 86.6 & 38.0 & 57.4 & 45.7 \\ SDGZSL * [10] & 69.6 & 78.2 & 73.7 & 73.0 & 77.5 & 75.1 & 86.1 & 89.1 & 87.8 & 39.1 & 60.7 & 47.5 \\ \hline ESTD-GZSL(Ours) & 65.1 & 73.4 & 70.0 & 65.3 & 64.5 & 64.9 & 85.2 & 91.9 & 88.4 & 36.5 & 63.2 & 46.2 \\ ESTD-GZSL *(Ours) & 66.6 & 81.2 & 73.2 & 74.5 & 80.2 & 77.4 & 83.8 & 93.6 & 88.5 & 35.2 & 74.8 & 47.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison in accuracy(%) on four datasets. We show the accuracies of seen and unseen classes and their harmonic mean for GZSL, which are denoted as U, S, and H. * means a finetuned backbone is used. Red font and blue font denote the highest and the second highest results, respectively.  
In Figure 4, we show the performance comparison between SDGZSL and \(h_{s}\), \(h_{r}\), and \(h_{s}\oplus h_{r}\). It can be seen that a classifier trained using only the feature's output from the semantic decoder can achieve comparable results to SDGZSL. This shows that using semantic-consistent features of the semantic decoder (_Dec_)'s output \(h_{r}\) can further improve performance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & \(\mathbf{U}\) & \(\mathbf{S}\) & \(\mathbf{H}\) \\ \hline Baseline & 73.0 & 77.5 & 75.1 \\ (a) & **75.0** & 79.6 & 77.2 \\ (b) & 74.6 & 80.0 & 77.2 \\ (c) & 73.8 & **81.4** & **77.4** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation study for different components of ESTD-GZSL on the CUB dataset. The best results are marked in **boldface**.  
Figure 4: \(U\), \(S\), and \(H\) accuracy (%) comparison between SDGZSL, \(h_{s}\), \(h_{r}\), and \(h_{s}\oplus h_{r}\).  
Figure 3: Zero-shot image retrieval result comparison between CVAE, SDGZSL, and ESTD-GZSL.