# Transfer Incremental Learning Using Data Augmentation
## 4 Experiments
### Results  
As a preliminary experiment, we aim to show that replacing the last layers of Inception V3 by the proposed method does not compromise the performances obtained on Imagenet ILSVRC 2012. The 5-top accuracy is 94.4% when we use TILDA with \(p=16\) and \(k=30\), and 96.5% when we use the last layers of Inception V3 to classify data. The accuracy obtained by TILDA approaches the one obtained by Inception V3, thus our method does not bring a considerable decrease in performances. The second experiment is performed on CIFAR10/100, ImageNet50 and ImageNet ILSVRC 2012, in which we show the contribution of data augmentation, NCM-inspired classification, and subspace division on classification accuracy. Therefore, we define three methods: TILDA-DA does not use data augmentation and classifies only the original image, TILDA-NCM disregards NCM inspired classification and uses \(k\) feature vectors randomly chosen per class, and TILDA-P, which is TILDA method with no splitting of vectors. Table 1 summarises the accuracy of TILDA, TILDA-DA, TILDA-NCM and TILDA-P, when performing one-shot learning (learn one example at a time). We notice that TILDA-DA, TILDA-NCM and TILDA-P reach lower accuracy than TILDA, which confirms that the combination of data augmentation with NCM-inspired classification and subspace division can achieve good performances. In the third experiment, we study the effect of both quantization parameters \(P\) and \(k\) on the accuracy of TILDA (see Figure 2). This experiment demonstrates that TILDA reaches best performances for \(P=16\). In the following, we perform experiments using TILDA with \(P=16\) and \(k=30\). Note that in order to be fair in comparison with other techniques, we do not perform data-augmentation during training or prediction in TILDA in the upcoming experiments. The fourth experiment stresses the effect of class-incremental learning. We adopt a class-incremental scenario (CI), in which methods are trained over streaming data providing all examples from one class simultaneously, one class at a time. We test and compare TILDA-DA, NCM, Learn++ and iCaRL on CIFAR10/100 and ImageNet50 (c.f. Figure 3). Learn++ adds one weak classifier each time a novel class is introduced, and iCaRL stores 30 feature vectors per class. We can see that TILDA-DA outperforms the other methods in this setting. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **TILDA** & **TILDA-DA** & **TILDA-NCM** & **TILDA-P** \\ \hline CIFAR100 & **69.6\%** & 65.3\% & 60.7\% & 67\% \\ CIFAR10 & **88.7\%** & 86.6\% & 84.11\% & 87\% \\ ImageNet50 & **76\%** & 74.4\% & 69.2\% & 72\% \\ ILSVRC 2012 & **94.4\%** & 91\% & 89.6\% & 90\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Accuracy on CIFAR10/100, ImageNet50 and ImageNet ILSVRC 2012. TILDA uses the following parameter: \(P=16\) and \(k=30\). We learn incrementally one example at a time. Figure 2: Evolution of the accuracy as a function of \(P\) and \(k\) for CIFAR10 (**left**), CIFAR100 (**middle**) and ImageNet50 (**right**). The next experiment illustrates the behaviour of the accuracy when trying to obtain incremental information from new examples of the same class. We adopt an example-incremental scenario (EI), in which we train the method over streaming data providing new examples without introducing new classes. We test and compare TILDA-DA, NCM, NN, Learn++ and BRIL on CIFAR10/100 and ImageNet50. We divide these datasets into 10 equal size parts, each part containing 5000 examples (500/50 example per class) for CIFAR10/100 and 4500 examples (90 per class) for ImageNet50, and learn one part at a time. Learn++ adds one weak classifier each time a new part is learned. Figure 4 shows that all methods handle example-incremental learning and improve their accuracy each time they learn new information provided by new examples. TILDA-DA consistently obtains higher accuracy than Learn++, NCM, NN and BRIL regardless of the quantity of provided data.