News is a source of information to know about progress in the various areas of life all across the globe. However, the volume of this information is high, and getting benefits from the available information becomes difficult. Moreover, the frequency of fake news is increasing significantly and used to fulfill a particular agenda. This led to research on the classification of news to prevent the spread of disinformation. In this work, we use Adversarial Training as a means of regularization for fake news classification. We train two transformed-based encoder models using adversarial examples that help the model learn noise invariant representations. We generate these examples by perturbing the model's word embedding matrix, and then we fine-tune the model on clean and adversarial examples simultaneously. We train and evaluate the models on the Buzzfeed Political News and Random Political News datasets. Results show consistent improvements over the baseline models when we train models using adversarial examples. Experiments show that Adversarial Training improves the performance by 1.25% over the BERT baseline, 2.05% over the Longformer baseline for the Random Political News dataset, 1.25% over the BERT baseline and 0.9% over the Longformer baseline for Buzzfeed Political News dataset in terms of F1-score.

The latest standard for video coding is versatile video coding (VVC) / H.266 which is developed by the joint video exploration team (JVET). Its coding structure is a multi-type tree (MTT) structure. There are two types of trees under the umbrella of the MTT structure. The first one is called a ternary tree (TT) and the second one is a binary tree (BT). Due to the use of brute force quest for residual rate distortion the quad tree and multi-type tree (QTMT) structure of coding unit (CU) split and contributes over 98% of the encoding time. This structure is efficient in coding, however increases computational complexity. Current paper proposes a deep learning technique to predict the QTMT based CU split rather than just the brute-force QTMT method to substantially speed up the time of encoding process for VVC/H.266 intra mode. In the first phase we developed an extensive database containing the ample CU splitting patterns with various streaming videos that is able to encourage the significant decrease of VVC/H.266 complexity by using data driven methods. in the Second phase, in accordance with the dynamic QT-MT structure at numerous locations, we suggest a multi-level exit CNN (MLE-CNN) model with a redundancy removal mechanism at different levels to determine the CU partition. In the third phase, for the training of MLE-CNN model we have established the adaptive loss function and analyzing the both unknown number of partition modes and the focus on RD cost minimization. Finally, a variable threshold decision system is established to achieve the targeted low complexity and RD performance. Ultimately experimental findings show that VVC/H.266 encoding time has reduced to 69.11% from 47.91% with insignificant bjontegaard delta bit rate (BDBR) to 2.919% from 1.023% which performs better than the existing futuristic and modern approaches.

Virtual cloth fitting network has an increasing demand with a growing online shopping trend to map target clothes on reference subject. Previous research depicts limitations in the generation of promising deformed clothes on the wearer's body while retaining the design features of cloth-like logo, text and wrinkles. The proposed model first learns thin-plate spline transformations to warp images according to body shape, followed by a try-on module. The former model combines deformed cloth with a rendered image to generate composition mask and outputs target body without blurry clothes while preserving critical requirements of the wearer. Experiments are performed on the Zalando dataset and the model produces fine richer details and promised generalized results.