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The project implements Siamese Network with Triplet Loss in Keras to learn meaningful image representations in a lower-dimensional space. By training on the MNIST dataset, it creates a powerful architecture and implements Triplet Loss function. The resulting model enables applications like image search, recommendation systems, and image clustering.
The repository of SiamHAN, an IPv6 address correlation model on TLS encrypted traffic. The work has been accepted as USENIX Security 2021 accepted Paper.
Implementation of state-of-the-art NLP models using transformers for tasks including machine translation, text-summarization, chatbots, and question answering.
The Facenet paper of 2015 proposed an interesting solution for huge multiclass problems. Instead of the traditional approach, we try to learn a similarity function i.e. degree of difference between 2 inputs. If the degree of difference between the inputs is less than a threshold then the inputs are classified as similar else different.