Their proposed framework outperforms state-of-the-art approaches for 3D reconstructions from 2D and 2.5D data, achieving 12 percent better performance on average in the ShapeNet benchmark dataset and up to 19 percent for certain classes of objects.
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.
The crowdsourcing produced 111.25 hours of video from 54 non-expert demonstrators to build “one of the largest, richest, and most diverse robot manipulation datasets ever collected using human creativity and dexterity.”
In a bid to raise awareness of the threats posed by climate change, the Mila team recently published a paper that uses GANs to generate images of how climate events may impact our environments — with a particular focus on floods.