# Vision Transformers and Transfer Learning Approaches for Arabic Sign Language Recognition
## 3 Methodology
### Transfer Learning for ArSL Classification
Inception [71] and InceptionResNet [72] were fine-tuned for ArSL in this work. The Inception architecture, also known as GoogleNet, is characterized by its use of an Inception module designed to capture multiscale features in an efficient manner. It achieves this by using multiple convolutional filters of different sizes (\(1\times 1\), \(3\times 3\), and \(5\times 5\)) in parallel and concatenating their outputs. By doing so, the Inception module can capture features at different scales without significantly increasing the number of parameters. InceptionResNet, on the other hand, combines the Inception architecture with residual connections to allow the network to directly propagate information from one layer to a deeper layer. This helps to alleviate the problem of vanishing gradients and enables the network to learn more easily, even when it becomes very deep. The combination of the Inception module and residual connections in InceptionResNet results in a powerful and efficient architecture, has demonstrated state-of-the-art performance on various benchmark datasets, and has been widely adopted in both research and industry applications. We utilized two variants called InceptionV3 and InceptionResNetV2 for ArSL classification. Xception, proposed in 2016, stands for \"Extreme Inception\", as it is an extension of Google's Inception architecture [73]. The main goal of Xception is to improve the efficiency and of deep learning models. It achieves this by replacing traditional convolutional layers with a modified version called depthwise separable convolutions which separate the spatial filtering and channel filtering processes. It applies spatial filtering on each input channel separately, followed by a \(1\times 1\) convolution that combines the filtered outputs. This reduces the number of parameters and allows for better learning capabilities. We utilized Xception in our study and evaluated it for ArSL classification. DenseNet, developed in [74], has a primary building block called the dense block, which consists of a series of layers that are connected to each other in a dense manner. Within a dense block, each layer receives the feature maps of all preceding layers as input, resulting in highly connected feature maps. This encourages feature reuse and enables the network to capture more diverse and abstract features. To ensure that the network does not become excessively large and computationally expensive, DenseNet incorporates a transition layer between dense blocks. The transition layer consists of a convolutional layer followed by a pooling operation, which reduces the number of feature maps, compresses information, and controls the growth of the network. We utilized one variant called DenseNet169. BiT (Big Transfer by Google Research) [75] was additionally utilized in this study as one of the large-scale pretrained models. It was designed to transfer knowledge from pretraining tasks to downstream tasks more effectively. The BiT model follows a two-step process: pretraining and transfer learning. During pretraining, the model is trained on large datasets containing diverse and unlabeled images. The goal is to learn general visual representations that capture various concepts and patterns in the images. This helps the model develop a strong understanding of the visual world. After pretraining, the knowledge learned by the BiT model is transferred to specific downstream tasks. Transfer learning involves fine-tuning the pretrained model on a smaller labeled dataset that is specific to the target task. By starting with a pretrained model, the transfer learning process benefits from the rich visual representations learned during pretraining. This often leads to improved performance and faster convergence on the target task compared to training a model from scratch. BiT offers a range of model variations, each with a different size and capacity. In this study, BiT helps accelerate transfer learning by fine-tuning it on our ArSL dataset.