SRI Blog

 - By MALLIKARJUN B R and AYUSH TEWARI, MPI for Informatics, SIC, Germany and 9 others Paper Link Abstract Photorealistic editing of head portraits is a challenging task as humans are very sensitive to inconsistencies in faces. Paper present an approach for high-quality intuitive editing of the camera viewpoint and scene illumination (parameterised with an environment map) in a portrait image. This requires our method to capture and control the full reflectance field of the person in the image. Most editing approaches rely on supervised learning using training data captured with setups such as light and camera stages. Such datasets are expensive to acquire, not readily available and do not capture all the rich variations of in-the-wild portrait images. In addition, most supervised approaches only focus on relighting and do not allow camera viewpoint editing.  Paper present a method that learns from limited supervised training data. The training images only include people ...

  Other learning paradigms are about minimization;  Reinforcement learning is about maximization . The statement quoted above has been attributed to Harry Klopf, though it might only be accurate in sentiment. The statement may sound vacuous, since minimization can be converted to maximization simply via negation of an objective. However, further reflection reveals a deeper observation. Many learning algorithms aim to mimic observed patterns, minimizing differences between model and data. Reinforcement learning is distinguished by its open-ended view. A reinforcement learning agent learns to improve its behavior over time, without a prescription for eventual dynamics or the limits of performance. If the objective takes non-negative values, minimization suggests a well-defined desired outcome while maximization conjures pursuit of the unknown Video Courtesy:bdtechtalks.com What happens when AI Plays Hide and Seek 500 Times Paper By - Xiuyuan Lu, Benjamin Van Roy, Vikranth Dwara...

 He guo, Xiameng Qin, Jiaming Liu, Junyu Han, Jingtuo Liu, Errui Ding Department of Computer Vision Technology(VIS), Baidu Inc Paper link Abstract Extracting entity from images is a crucial part of many OCR applications, such as entity recognition of cards, invoices, and receipts. Most of the existing works employ classical detection and recognition paradigm. Paper proposes an Entity-aware Attention Text Extraction Network called EATEN , which is an end-to-end trainable system to extract the entities without any post-processing. In the proposed framework, each entity is parsed by its corresponding entity-aware decoder, respectively. Moreover, we innovatively introduce a state transition mechanism which further improves the robustness of entity extraction. In consideration of the absence of public benchmarks, we construct a dataset of almost 0.6 million images in three realworld scenarios (train ticket, passport and business card), which is publicly available at https://github.com/b...

-By Joana Lorenz , Maria Inês Silva, David Aparício, João Tiago Ascensão, Pedro Bizarro Feedzai Paper link                                     Git hub link Abstract Every year, criminals launder billions of dollars acquired from serious felonies (e.g., terrorism, drug smuggling, or human trafficking) harming countless people and economies. Cryptocurrencies, in particular, have developed as a haven for money laundering activity. Machine Learning can be used to detect these illicit patterns. However, labels are so scarce that traditional supervised algorithms are inapplicable. Here, we address money laundering detection assuming minimal access to labels. First, we show that existing state-of-the-art solutions using unsupervised anomaly detection methods are inadequate to detect the illicit patterns in a real Bitcoin transaction dataset. Image courtesy: BitCoin Magazine  ...

-By Riya R. Ganiga, Shaguftha Zuveria Kottur, Tallapalli Surabhi, A. Parkavi, Sini Anna Alex Ramaiah Institute of Technology, Bangalore, India Paper Link Abstract Compilers are used to translate between languages or representations. In neural networks, the input is usually in the form of a compute graph with tensor computations associated with nodes which needs to be translated into executable. The compiler plays an important role in this conversion, performing optimizations and lowering. It can be used as a bridge to target multiple hardware architectures from multiple front ends and hence is a major component for scalability of neural network frameworks. Also, the optimizations and conversions done by a compiler lead to reduction in time taken to train a particular network and its implementation. courtesy:  https://www.cs.utexas.edu/~roshan Code readability, ease of construction, time and space complexity and size of the code are some of the important features of a program c...

Angle based detection By  Hans-Peter Kriegel, Matthias Schubert, Arthur Zimek  Ludwig-Maximilians-Universität München  Oettingenstr. 67, 80538 München, Germany Ref Link PyOD By  Yue Zhao   Zain Nasrullah   Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada  Zheng Li jk  Northeastern University Toronto, Toronto, ON M5X 1E2, Canada I am combining two papers to summarize Anomaly detection. First one is Angle Based Outlier Detection (ABOD) and other one is python module that  uses ABOD along with over 20 other apis (PyOD) . This is third part in the series of Anomaly detection. First article exhibits survey that covered length and breadth of subject, Second article highlighted on data preparation and pre-processing.  Angle Based Outlier Detection. Angles are more stable than distances in high dimensional spaces for example the popularity of cosine-based sim...