# AutoRet: A Self-Supervised Spatial Recurrent Network for Content-Based Image Retrieval
## 4 Experiment
### Comparison  
In the comparison, we foremost evaluate the AutoRet architecture with two different backbones: DenseNet121 [57] and MobileNet [58] which are observed to be implemented in the AutoEmbedder framework [48; 49]. Table 2 depicts a comparison of AutoRet architecture with two distinct backbones. Moreover, benchmarks are also conducted with and without the SPP and ASPP mechanisms. The comparison explains that DenseNet121 performs better \begin{table}
\begin{tabular}{l l l} \hline \hline
**Dataset** & **Classes** & **Data** \\ \hline CIFAR-10 [52] & 10 & 60,000 \\ \hline MIRFlickr-25K [53] & 38 & 25,000 \\ \hline NUS-WIDE [54] & 80 & 270,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative analysis of the datasets. than the MobileNet framework. Further, adding SPP in both baselines greatly improves the retrieval performance. In the case of ASPP, the margin of improvement on CIFAR-10 tends to be higher than the SPP mechanism. CIFAR-10 contains low-resolution images up-sampled to \(128\times 128\) to feed the network. As ASPP provides an improved field of view, it enables better confidence for low-resolution images. Therefore, it can be concluded that ASPP performs better for low-resolution images. Consequently, fusing ASPP and SPP with the baselines improves the query performance. Table 3 exhibits a comparison of AutoRet with different models based on MAP. By examining the table, it can be noticed that GAN-based architectures mostly perform marginally. In contrast, the graph-based self-supervised model SGSH performs better in MIRFlickr-25K and performs inadequately in the other datasets. SGSH is based on a sparse graph; therefore, it only receives strong edges, rejecting the less important but useful connections. As a result, SGSH suffers from graph connectivity issues. Parallelly, SPQ uses contrastive loss [59] in model training. However, SPQ misses to properly aggregate the overall description of an image. Therefore, the system can be misled by the background of an image. Comparatively, AutoRet achieves a better margin of improvement than SPQ due to better feature localization, resolution-independent feature extraction, and reasonable feature aggregation. Figure 6 illustrates a precision-recall graph for some of the models in the benchmark. In the case of a precision-recall metric, SPQ offers to be a strong candidate with AutoRet. Although the SPQ and AutoRet perform similarly on CIFAR-10, AutoRet performs more promising than SPQ on other datasets. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Method** & \begin{tabular}{l} **CIFAR-10** \\ **(MAP)** \\ \end{tabular} & \
\begin{tabular}{l} **MIRFlickr-25K** \\ **(MAP)** \\ \end{tabular} &
\begin{tabular}{l} **NUS-WIDE** \\ **(MAP)** \\ \end{tabular} \\ \hline MobileNet & 0.682 & 0.671 & 0.582 \\ \hline MobileNet+SPP & 0.742 & 0.751 & 0.766 \\ \hline MobileNet+ASPP & 0.767 & 0.764 & 0.768 \\ \hline MobileNet+SPP+ASPP & 0.796 & 0.771 & 0.785 \\ \hline DenseNet121 & 0.716 & 0.688 & 0.610 \\ \hline DenseNet121+SPP & 0.781 & 0.782 & 0.785 \\ \hline DenseNet121+ASPP & 0.792 & 0.774 & 0.748 \\ \hline
**DenseNet121+SPP+ASPP** & 0.835 & 0.791 & 0.801 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: A comparison of different architectural constructions of the AutoRet. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Method** & \begin{tabular}{l} **CIFAR-10** \\ **(MAP)** \\ \end{tabular} & \
\begin{tabular}{l} **MIRFlickr-25K** \\ **(MAP)** \\ \end{tabular} &
\begin{tabular}{l} **NUS-WIDE** \\ **(MAP)** \\ \end{tabular} \\ \hline SADH & 0.377 & 0.481 & 0.563 \\ \hline BGAN & 0.562 & 0.695 & 0.730 \\ \hline BinGAN & 0.520 & 0.688 & 0.713 \\ \hline UDHP & 0.384 & 0.680 & 0.526 \\ \hline Distillhash & 0.287 & 0.708 & 0.621 \\ \hline DVB & 0.396 & 0.524 & 0.595 \\ \hline SPQ & 0.812 & 0.778 & 0.785 \\ \hline SGSH & 0.469 & 0.739 & 0.628 \\ \hline
**AutoRet (ours)** & 0.835 & 0.791 & 0.801 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: A comparison of different image retrieval systems.