The eighth edition of The Fifth Elephant will be held in Bangalore on 25 and 26 July. A thousand data scientists, ML engineers, data engineers and analysts will gather at the NIMHANS Convention Centre in Bangalore to discuss:
- Model management, including data cleaning, instrumentation and productionizing data science.
- Bad data and case studies of failure in building data products.
- Identifying and handling fraud + data security at scale
- Applications of data science in agriculture, media and marketing, supply chain, geo-location, SaaS and e-commerce.
- Feature engineering and ML platforms.
- What it takes to create data-driven cultures in organizations of different scales.
1. Meet Peter Wang, co-founder of Anaconda Inc, and learn about why data privacy is the first step towards robust data management; the journey of building Anaconda; and Anaconda in enterprise.
2. Talk to the Fulfillment and Supply Group (FSG) team from Flipkart, and learn about their work with platform engineering where ground truths are the source of data.
3. Attend tutorials on Deep Learning with RedisAI; TransmorgifyAI, Salesforce’s open source AutoML.
4. Discuss interesting problems to solve with data science in agriculture, SaaS perspective on multi-tenancy in Machine Learning (with the Freshworks team), bias in intent classification and recommendations.
5. Meet data science, data engineering and product teams from sponsoring companies to understand how they are handling data and leveraging intelligence from data to solve interesting problems.
Why you should attend?
- Network with peers and practitioners from the data ecosystem
- Share approaches to solving expensive problems such as cleanliness of training data, model management and versioning data
- Demo your ideas in the demo session
- Join Birds of Feather (BOF) sessions to have productive discussions on focussed topics. Or, start your own Birds of Feather (BOF) session.
Full schedule published here: https://hasgeek.com/fifthelephant/2019/schedule
For more information about The Fifth Elephant, sponsorships, or any other information call +91-7676332020 or email email@example.com
JSFoo:VueDay 2019 sponsors:
Siamese Triple Ranking Convolution Network in Signature Forgery Detection
Session type: Full talk of 40 mins
Identifying a credible signature match based on a base signature of a person is an age old problem. Despite recent automation and advances in this field using image recognition, a lot remains to be explored. We have developed an intelligent framework which can automatically detect a forged signature even if it is highly skilled, based on the developed feature embeddings and the corresponding algorithm. Siamese Triplet Convolution Neural Network is used to generate the feature embeddings for the signature images followed by a generalized Logistic Regression model to detect forgery. On the widely used SigComp dataset, our system achieves an accuracy of 96% in detecting forged signatures. Once the model is trained, it requires just one base image to determine whether another signature image is genuine or fraudulent with one shot learning. This algorithmic framework can be used in multiple commercial settings. One such example is testing customer or employee signatures on documents against a corresponding base signature saved beforehand.
The differences between the images of a genuine signature and its skilled forgery is at times very minute and is challenging to detect by even a trained eye. Deep Triplet loss function is a very powerful loss function used in the industry for face recognition. We have created our custom triplet model architecture with modified MobileNet CNN and dense layers with triplet loss function. Based on this loss, the image embeddings are created in such a way that the dissimilarity between the anchor image and positive image must be low and the dissimilarity between the anchor image and the negative image must be high for every triplet. This kind of architecture ensures that even small differences in signatures can be captured in order to flag a skilled forgery effectively.
The talk will cover the following:
• Introduction to the business objective of Signature Fraud Detection and the algorithmic challenges associated with Signature Fraud.
• A detailed domain background on various types of Forgery and their complexities.
• Description of the various Dataset sources and the complexities in data collection and forming the training set with image triplets.
• Introduction to Siamese Triplet CNN Architecture and one-shot learning applications in fraud detection.
• Transfer Learning and finetuning the siamese triplet model using MobileNet architecture and its benefits.
• Logistic loss function implementation with the above architecture to form a robust method in detecting fraud signature.
• Final Framework architecture of the entire methodology and the accuracy attained using the same.
• Reference and Conclusion.
Deep Learning,Computer Vision,Machine Learning, Image Processing.
Ojaswini Chhabra received M.S. (Quantitative Economics) from Indian Statistical Institute, Delhi and B.A. (Hons.) Economics from University of Delhi in 2016 and 2014 respectively. She currently works as Senior Statistical Analyst in Walmart Labs, Bangalore. She has worked on several classification and optimization-based problems and filed patents for the same. Her research interests include natural language processing and computer vision.
Address: G-504, Tower 6, Adarsh Palm Retreat, Bellandur, Bengalore - 560103 Contact: +91-8285671581
Souradip Chakraborty received M.S. (Quality Management Science) from Indian Statis- tical Institute, Bangalore and B.E. (Engg.) Electronics and Instrumentation from Jadavpur University in 2016 and 2014 respectively. He is currently working as a Statistical Analyst in Walmart Labs, Bangalore. His current field of research interest lies in representation learning, mixed-space representations, NLP, Vision, and he has several patents filed (as a part of Walmart Labs) in the field of AI and Machine Learning applications.
Address: 603, Vasudeva Residency, 4th Cross Rd, Bansawadi, Bangalore - 560043 Contact: +91-9038790361