Cutting the Error by Half: Investigation of Very Deep CNN and Advanced Training Strategies for Document Image Classification

We present an exhaustive investigation of recent Deep Learning architectures, algorithms, and strategies for the task of document image classification to finally reduce the error by more than half. Existing approaches, such as the DeepDocClassifier, apply standard Convolutional Network architectures with transfer learning from the object recognition domain. The contribution of the paper is threefold: First, it investigates recently introduced very deep neural network architectures (GoogLeNet, VGG, ResNet) using transfer learning (from real images). Second, it proposes transfer learning from a huge set of document images, i.e. 400,000 documents. Third, it analyzes the impact of the amount of training data (document images) and other parameters to the classification abilities. We use two datasets, the Tobacco-3482 and the large-scale RVL-CDIP dataset. We achieve an accuracy of 91.13% for the Tobacco-3482 dataset while earlier approaches reach only 77.6%. Thus, a relative error reduction of more than 60% is achieved. For the large dataset RVL-CDIP, an accuracy of 90.97% is achieved, corresponding to a relative error reduction of 11.5%

Post-operative Functional Outcome in Fracture Distal Shaft of Femur Treated with Retrograde Nailing

Background: To determine the functional outcome of retrograde femoral nailing in terms of Tegner Lysholm score for distal femoral farctures. Methods: This prospective cross-sectional study was done at the Department of Orthopaedics, Benazir Bhutto Hospital, Rawalpindi from 20th January 2018 to 19th January 2019. The study comprised of 35 patients who presented with simple, extra-articular distal femur fracture. All patients were treated with retrograde femoral nailing using anterior (Para patellar) approach. Functional outcome was assessed at 6-months using Tegner Lysholm score. Data was analyzed using SPSS version 22. Results: Out of the 35 patients, majority were females (60%). Mean age of patients was 51.4±11.4 years. Most common age group in males was 41-50 years while in females it was 61 years and above. Average BMI of patients was 22.7±2.8 kg/m2. At 6-months post-operatively, Tegner Lysholm score was between 86-100, 71-85 and 56-70 in 65.7%, 25.7% and 8.6% cases, respectively. Tegner Lysholm score between genders was insignificant (p=0.4). Conclusion: Retrograde femoral nailing has excellent functional outcome in patients with extra-articular distal femoral fractures

If You Like It, GAN It. Probabilistic Multivariate Times Series Forecast With GAN

The contribution of this paper is two-fold. First, we present ProbCast - a novel probabilistic model for multivariate time-series forecasting. We employ a conditional GAN framework to train our model with adversarial training. Second, we propose a framework that lets us transform a deterministic model into a probabilistic one with improved performance. The motivation of the framework is to either transform existing highly accurate point forecast models to their probabilistic counterparts or to train GANs stably by selecting the architecture of GAN's component carefully and efficiently. We conduct experiments over two publicly available datasets namely electricity consumption dataset and exchange-rate dataset. The results of the experiments demonstrate the remarkable performance of our model as well as the successful application of our proposed framework