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Found 7 papers, 0 papers with code
Federated Learning Method for Preserving Privacy in Face Recognition System
Federated learning facilitates the training of a shared model without necessitating the sharing of individual private data, achieving this by training models on decentralized edge devices housing the data.
Unsupervised Deep Learning Image Verification Method
In this work, we propose a method to narrow this gap by leveraging an autoencoder to convert the face image vector into a novel representation.
Deep Learning Based Face Recognition Method using Siamese Network
Achieving state-of-the-art results in face verification systems typically hinges on the availability of labeled face training data, a resource that often proves challenging to acquire in substantial quantities.
Autoencoder Based Face Verification System
Initially, an autoencoder is trained in an unsupervised manner using a substantial amount of unlabeled training dataset.
Image Clustering using Restricted Boltzman Machine
In this work, we propose the use of RBMs to the image clustering tasks.
Transferring Monolingual Model to Low-Resource Language: The Case of Tigrinya
Thus, using XLNet language model, we demonstrate competitive performance with mBERT and a pre-trained target language model on the cross-lingual sentiment (CLS) dataset and on a new sentiment analysis dataset for low-resourced language Tigrinya.
Do Autonomous Agents Benefit from Hearing?
In this work, we propose the use of audio as complementary information to visual only in state representation.
