# Learning Class-Specific Features with Class Regularization for Videos
## 4 Experiments
### Main Results  
A comparison between results of models trained from scratch on Kinetics-400 appears in Table 2. Existing networks consider a complete change in the overall architecture or convolution operations in models, which is computationally challenging given the large memory requirements of spatiotemporal models. New models need to be trained for a significant number of iterations in order to achieve mild improvements, while additionally using large datasets [34; 35] for pretraining. In contrast, the proposed _Class Regularization_ method is used on top of existing architectures and only requires fine-tuning the dimensionality correspondence between the number of features in a specific layer and the features that are used for class predictions. Overall, the largest improvements were observed on networks with larger number of layers, such as ResNet101-3D, in comparison to networks with lower number of layers, with the best performing architectures being I3D and ResNet101-3D with _Class Regularization_ with 67.8% top-1 accuracy and 67.7% top-1 accuracy, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer Name** & **3D ResNet101 (w/o CN)** & **3D Wide ResNet 50 (w/o CN)** & **I3D (w/o CN)** \\ \hline \(con3d_{1}\) & & \(7\times 7\times 7,64\) conv & \\ \hline \(con3d_{2}\) & \(\begin{bmatrix}1\times 1\times 1\\ 3\times 3\times 3\\ 1\times 1\end{bmatrix}(\times 64)\times 3\) & \(\begin{bmatrix}1\times 1\times 1\\ 3\times 3\times 3\\ 1\times 1\end{bmatrix}(\times 128)\times 3\) & \(\begin{bmatrix}1\times 1\times 1\ &1\times 1\ &1\times 1\times 1\ &3\times 3\times 3,(pool)\\ 3\times 3\times 3\ &3\times 3\times 3\ &1\times 1\times 1\end{bmatrix}( \times 480)\times 2\) \\ \hline \(ClassReg_{1}\) & - / \(\alpha=0.9\) & - / \(\alpha=0.9\) & - / \(\alpha=0.8\) \\ \hline \(con3d_{2}\) & \(\begin{bmatrix}1\times 1\times 1\\ 3\times 3\times 3\\ 1\times 1\end{bmatrix}(\times 128)\times 4\) & \(\begin{bmatrix}1\times 1\times 1\\ 3\times 3\times 3\\ 1\times 1\end{bmatrix}(\times 256)\times 4\) & \(\begin{bmatrix}1\times 1\times 1\ &1\times 1\times 1\ &1\times 1\times 1\ &3\times 3\times 3,(pool)\\ 3\times 3\times 3\ &3\times 3\times 3\ &1\times 1\times 1\end{bmatrix}( \times 832)\times 5\) \\ \hline \(ClassReg_{2}\) & - / \(\alpha=0.8\) & - / \(\alpha=0.8\) & - / \(\alpha=0.7\) \\ \hline \(con3d_{2}\) & \(\begin{bmatrix}1\times 1\times 1\\ 1\times 1\end{bmatrix}(\times 256)\times 23\) & \(\begin{bmatrix}1\times 1\times 1\\ 3\times 3\times 3\\ 1\times 1\end{bmatrix}(\times 512)\times 6\) & \(\begin{bmatrix}1\times 1\times 1\ &1\times 1\times 1\ &1\times 1\times 1\ &3\times 3\times 3,(pool)\\ 3\times 3\times 3\ &3\times 3\times 3\ &1\times 1\times 1\end{bmatrix}( \times 1024)\times 2\) \\ \hline \(ClassReg_{3}\) & - / \(\alpha=0.7\) & - / \(\alpha=0.7\) & - / \(\alpha=0.6\) \\ \hline \(con3d_{2}\) & \(\begin{bmatrix}1\times 1\times 1\\ 3\times 3\times 3\\ 1\times 1\end{bmatrix}(\times 512)\times 3\) & \(\begin{bmatrix}1\times 1\times 1\\ 3\times 3\times 3\\ 1\times 1\end{bmatrix}(\times 1024)\times 3\) & - \\ \hline \(ClassReg_{4}\) & () / \(\alpha=0.6\) & - / \(\alpha=0.6\) & - \\ \hline \hline \multicolumn{3}{c}{global average pool, softmax unit group} \\ \hline \end{tabular}
\end{table}
Table 1: Architectures of 3D convolution models with and without class regularization. We note that in the _Class Regularized_ networks, only an eighth of the additional parameters are trainable, with the rest corresponding to nontrainable class weights tensor duplicates.