[GIDS 2021] Signature Verification in Banks using Few Shot Learning

Tue, 27 Apr 2021 12:30:00 +0000

Description

In case of standard image classification task, the input image is fed into a series of layers, and finally at the output we generate a probability distribution over all the classes. But it requires a large number of images. In offline signature verification scenario, we neither have enough signature for each signer and the total number signers is huge as well as dynamically changing. Thus, the cost of data collection and periodical retraining is too high. On the other hand, in a few shot image classification, we require only a few signatures for each signer, hence the name Few Shot.

The speaker will discuss the following:

Introduction to Few Shot Learning
The architecture of Siamese Network
How to train Offline signature verification system using Siamese Networks on real life data
Tools to build such model
How to deploy such model on cloud
How to serve such model in real time
Pros and Cons of our approach

Learn what few shot learning is and how to build and deploy such models on the cloud to solve various classification tasks on image data with very limited amount of data.

Presentation Video

Gidsindia2021 | Tuhin Sharma

[GIDS 2021] Signature Verification in Banks using Few Shot Learning

Description