Monthly Archives: January, 2021

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Google Researchers Introduce a New Framework (TReCS) For Text-to-Image Generation

Deep neural networks based on Generative Adversarial Networks (GANs) have facilitated end-to-end trainable photo-realistic text-to-image generation. Many methods also use intermediate scene graph representations...

Google Trains A Trillion-Parameter AI Language Model That Is Almost 6 Times Bigger Than GPT-3

Google researchers have developed techniques that can now train a language model with more than a trillion parameters. The 1.6 trillion parameter model is the...

Researchers From Tsinghua University Introduces AutoGL: An AutoML Framework For Machine Learning On Graph

The primary aim of AutoML or Automated machine learning is to reduce the skilled human effort required for building machine learning and deep learning...

Researchers From Computer Vision Center (CVC) And The University Of Barcelona Conducted A Study That Results In Improved Accuracy On Face Verification Tasks In...

Automatic face recognition is being widely adopted by private and governmental organizations worldwide for various legitimate and beneficial purposes, such as improving security. However,...

OpenAI Introduces CLIP: A Neural Network That Efficiently Learns Visual Concepts From Natural Language Supervision

OpenAI introduced a neural network, CLIP, which efficiently learns visual concepts from natural language supervision. CLIP, also called Contrastive Language–Image Pre-training, is available to be...

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