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DeepMind’s Meta-Learning Sparse Compression Networks Set New SOTA on Diverse Modality Data Compression
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In the new paper Meta-Learning Sparse Compression Networks, a DeepMind research team proposes steps for scaling implicit neural representations (INRs). The resulting meta-learning sparse compression networks can represent diverse data modalities such as images, manifolds, signed distance functions, 3D shapes, and scenes, achieving state-of-the-art results on some of them.
The post DeepMind’s Meta-Learning Sparse Compression Networks Set New SOTA on Diverse Modality Data Compression first appeared on Synced.
ai artificial intelligence compression data data compression deepmind deep-neural-networks learning machine learning machine learning & data science meta meta-learning ml networks research set sota technology