Basic machine learning algorithms or transfer learning models work well for language categorization, but these models require a vast volume of annotated data. We need a better model to tackle the problem because labeled data is scarce. This problem may have a solution in GAN-BERT. To classify Bengali text, we developed a GAN-BERT based model, which is an adapted version of BERT. We used two different datasets for this purpose. One is a hate speech dataset, while the other is a fake news dataset. To understand how the GAN-Bert and basic BERT models behave with Bangla datasets, we experimented with both. With a small quantity of data, we were able to get a satisfactory result using GAN-BERT. We also demonstrated how the accuracy increases as the number of training samples increases. A comparison of performance between traditional BERT based Bangla-BERT and our GAN-Bangla-BERT model is also shown here, where we can see how these models react to a small number of labeled data.

Jamdani is the strikingly patterned textile heritage of Bangladesh. The exclusive geometric motifs woven on the fabric are the most attractive part of this craftsmanship having a remarkable influence on textile and fine art. In this paper, we have developed a technique based on Generative Adversarial Network that can learn to generate entirely new Jamdani patterns from a collection of Jamdani motifs that we assembled; the newly formed motifs can mimic the appearance of the original designs. Users can input the skeleton of the desired pattern in terms of rough strokes and our system finalizes the input by generating the complete motif which follows the geometric structure of real Jamdani ones. To serve this purpose, we collected and preprocessed a dataset containing a large number of Jamdani motifs images from authentic sources via fieldwork and applied a state-of-the-art method called pix2pix on it. To the best of our knowledge, this dataset is currently the only available dataset of Jamdani motifs in digital format for computer vision research. Our experimental results of the pix2pix model on this dataset show satisfactory outputs of computer-generated images of Jamdani motifs and we believe that our work will open a new avenue for further research.