P-SN es un clasificador de noticias falsas que utiliza características independientes del lenguaje representadas como series de tiempo. \\ P-SN is a fake news classifier that uses language-agnostic features represented as time series.

This project is concerned with my participating in the RuNNE competition https://github.com/dialogue-evaluation/RuNNE

This is a face verification application made using Siamese Neural Network.

An authentication system that uses a Siamese neural network to compare fingerprint scans.

Signature verification using siamese neural networks and pytorch.

Design of a Face recognition payment system prototype

Verification of off-line signatures is one of the most challenging tasks in biometrics and document forensic science. In this thesis, we deal with Convolutional Siamese Network model which is capable of doing verification of Bengali and Hindi Signature. One particular advantage of Siamese Neural networks is the ability to generalize to new class…

An implementation for few computer vision models

Tensorflow-2 Implementation of the paper Siamese Capsule Network for End-to-End Speaker Recognition in the Wild

Image Similarity measure using Deep Siamese Network

Signature Verification using Siamese Neural Networks

Repository containing code for Siamese SIREN: Audio Compression with Implicit Neural Representations. Published as a workshop paper at ICML 2023 neural compression workshop.

See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks (CVPR19)

A CNN used to fetch a person's identity based on their face.

This project describes an algorithm based on Siamese Neural Network architecture for a snap-shot app to detect DVD covers and classify them.

My Ph.D. thesis "Visual Object Tracking Using Siamese Neural Networks"

In 2020 I experimented with the Raspberry Pi camera to create a facial recognition system using Siamese Neural Networks.

Execution of COMPUTE VISION methods for image classification Using: Siamese Network, Convolutional Neural Network, Variational Auto Encoder

We analyze the handwritten AND image dataset from various writers using Variational Autoencoders, Siamese Network and then pass it through a Multi-Task Learning process to get the 15 features of the images directly. This is then used to compare the handwriting of the writers

Face recognition using Pytorch, Facenet and Siamese Network