FRILL is a Non-Semantic Speech model that is 40 percent the size of TRILL and can compute over 32 times faster on mobile phones.
Representation learning is a machine learning (ML) method that trains a model to discover prominent features. It may apply to a wide range of downstream tasks– including Natural Language Processing (BERT and ALBERT) and picture analysis and classification (Inception layers and SimCLR). Last year, researchers developed a baseline for comparing speech representations and a new, general-purpose speech representation model, TRILL. It is based on temporal proximity and attempts to map speech that happens close together in time to a lower-dimensional embedding space that captures temporal proximity. https://analyticsindiamag.com/on-device-speech-representation-using-tensorflow-lite/
TensorFlow Lite Object Detection using Raspberry Pi and Pi Camera. Get it working! Result: wow, just works, completed in a short time.
This article will explain how to reduce the size of an image classification machine learning model for mobile using TensorFlow Lite, in order to make it fit and work on mobile devices. Predicting whether a person in an image is wearing a mask or not. Training an Image Classification Model for Mobile using TensorFlow Lite
We’re going to discuss how to implement a housing price prediction machine learning model for mobile using TensorFlow Lite. We’ll learn how to train a TensorFlow Lite neural network for regression that provides a continuous value prediction, specifically in the context of housing prices.
Training a Keras-VGG16 model to facial emotion recognition on low-power devices. In here, Detect Facial Emotions on Mobile and IoT Devices Using TensorFlow Lite
A demo code of training and testing using Tensorflow. This is a demo code of training and testing \[ProbFace\] using Tensorflow. ProbFace is a reliable Probabilistic Face Embeddging (PFE) method.