Researchers “posit that the universes of knowledge and experience available to NLP models can be defined by successively larger world scopes: from a single corpus to a fully embodied and social context.”
Just as biologists gain insights into organisms by putting model specimens under their microscopes, AI Microscope was designed to help researchers analyze the features that form inside leading CV models.
In a bid to generate high-resolution images showing realistic daytime changes while keeping accurate scene semantics, researchers have proposed a novel image-to-image translation model, HiDT (High Resolution Daytime Translation).
Researchers from the University of Chicago Oriental Institute (OI) and the Department of Computer Science have introduced an artificial intelligence tool called DeepScribe designed to read cuneiform tablets from 25 centuries ago.
Researchers have introduced a novel hybrid continual learning algorithm, Adversarial Continual Learning, which aims to enable the persistent explicit or implicit replay of experiences by storing original samples
Researchers have proposed a new image generative model that leverages the hierarchical space of deep features learned by pretrained classification networks and provides a unified and versatile framework for image generation and manipulation tasks.
Researchers proposed an automatic structured pruning framework, AutoCompress, which adopts the 2018 ADMM-based weight pruning algorithm and outperforms previous automatic model compression methods while maintaining high accuracy.
Proposed by researchers from the Rutgers University and Samsung AI Center in the UK, CookGAN uses an attention-based ingredients-image association model to condition a generative neural network tasked with synthesizing meal images.
Google teamed up with researchers from Synthesis AI and Columbia University to introduce a deep learning approach called ClearGrasp as a first step to teaching machines how to “see” transparent materials.
Researchers from Google Brain and Carnegie Mellon University have released models trained with a semi-supervised learning method called “Noisy Student” that achieve 88.4 percent top-1 accuracy on ImageNet.