Enhancing GANs with Contrastive Learning-Based Multistage Progressive Finetuning SNN and RL-Based External Optimization

The application of deep learning in cancer research, particularly in early diagnosis, case understanding, and treatment strategy design, emphasizes the need for high-quality data. Generative AI, especially Generative Adversarial Networks (GANs), has emerged as a leading solution to challenges like class imbalance, robust learning, and model training, while addressing issues stemming from patient privacy and the scarcity of real data. Despite their promise, GANs face several challenges, both inherent and specific to histopathology data. Inherent issues include training imbalance, mode collapse, linear learning from insufficient discriminator feedback, and hard boundary convergence due to stringent feedback. Histopathology data presents a unique challenge with its complex representation, high spatial resolution, and multiscale features. To address these challenges, we propose a framework consisting of two components. First, we introduce a contrastive learning-based Multistage Progressive Finetuning Siamese Neural Network (MFT-SNN) for assessing the similarity between histopathology patches. Second, we implement a Reinforcement Learning-based External Optimizer (RL-EO) within the GAN training loop, serving as a reward signal generator. The modified discriminator loss function incorporates a weighted reward, guiding the GAN to maximize this reward while minimizing loss. This approach offers an external optimization guide to the discriminator, preventing generator overfitting and ensuring smooth convergence. Our proposed solution has been benchmarked against state-of-the-art (SOTA) GANs and a Denoising Diffusion Probabilistic model, outperforming previous SOTA across various metrics, including FID score, KID score, Perceptual Path Length, and downstream classification tasks.

C-RADAR: A Centralized Deep Learning System for Intrusion Detection in Software Defined Networks

The popularity of Software Defined Networks (SDNs) has grown in recent years, mainly because of their ability to simplify network management and improve network flexibility. However, this also makes them vulnerable to various types of cyber attacks. SDNs work on a centralized control plane which makes them more prone to network attacks. Research has demonstrated that deep learning (DL) methods can be successful in identifying intrusions in conventional networks, but their application in SDNs is still an open research area. In this research, we propose the use of DL techniques for intrusion detection in SDNs. We measure the effectiveness of our method by experimentation on a dataset of network traffic and comparing it to existing techniques. Our results show that the DL-based approach outperforms traditional methods in terms of detection accuracy and computational efficiency. The deep learning architecture that has been used in this research is a Long Short Term Memory Network and Self-Attention based architecture i.e. LSTM-Attn which achieves an Fl-score of 0.9721. Furthermore, this technique can be trained to detect new attack patterns and improve the overall security of SDNs.

ChartEye: A Deep Learning Framework for Chart Information Extraction

The widespread use of charts and infographics as a means of data visualization in various domains has inspired recent research in automated chart understanding. However, information extraction from chart images is a complex multitasked process due to style variations and, as a consequence, it is challenging to design an end-to-end system. In this study, we propose a deep learning-based framework that provides a solution for key steps in the chart information extraction pipeline. The proposed framework utilizes hierarchal vision transformers for the tasks of chart-type and text-role classification, while YOLOv7 for text detection. The detected text is then enhanced using Super Resolution Generative Adversarial Networks to improve the recognition output of the OCR. Experimental results on a benchmark dataset show that our proposed framework achieves excellent performance at every stage with F1-scores of 0.97 for chart-type classification, 0.91 for text-role classification, and a mean Average Precision of 0.95 for text detection.

Object Detection for Vehicle Dashcams using Transformers

The use of intelligent automation is growing significantly in the automotive industry, as it assists drivers and fleet management companies, thus increasing their productivity. Dash cams are now been used for this purpose which enables the instant identification and understanding of multiple objects and occurrences in the surroundings. In this paper, we propose a novel approach for object detection in dashcams using transformers. Our system is based on the state-of-the-art DEtection TRansformer (DETR), which has demonstrated strong performance in a variety of conditions, including different weather and illumination scenarios. The use of transformers allows for the consideration of contextual information in decisionmaking, improving the accuracy of object detection. To validate our approach, we have trained our DETR model on a dataset that represents real-world conditions. Our results show that the use of intelligent automation through transformers can significantly enhance the capabilities of dashcam systems. The model achieves an mAP of 0.95 on detection.