Modality-Agnostic Variational Compression of Implicit Neural Representations

• URL: http://arxiv.org/abs/2301.09479v3
• Date: Fri, 7 Apr 2023 11:29:26 GMT
• Title: Modality-Agnostic Variational Compression of Implicit Neural Representations
• Authors: Jonathan Richard Schwarz and Jihoon Tack and Yee Whye Teh and Jaeho Lee and Jinwoo Shin
• Abstract summary: We introduce a modality-agnostic neural compression algorithm based on a functional view of data and parameterised as an Implicit Neural Representation (INR) Bridging the gap between latent coding and sparsity, we obtain compact latent representations non-linearly mapped to a soft gating mechanism. After obtaining a dataset of such latent representations, we directly optimise the rate/distortion trade-off in a modality-agnostic space using neural compression.
• Score: 96.35492043867104
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a modality-agnostic neural compression algorithm based on a functional view of data and parameterised as an Implicit Neural Representation (INR). Bridging the gap between latent coding and sparsity, we obtain compact latent representations non-linearly mapped to a soft gating mechanism. This allows the specialisation of a shared INR network to each data item through subnetwork selection. After obtaining a dataset of such latent representations, we directly optimise the rate/distortion trade-off in a modality-agnostic space using neural compression. Variational Compression of Implicit Neural Representations (VC-INR) shows improved performance given the same representational capacity pre quantisation while also outperforming previous quantisation schemes used for other INR techniques. Our experiments demonstrate strong results over a large set of diverse modalities using the same algorithm without any modality-specific inductive biases. We show results on images, climate data, 3D shapes and scenes as well as audio and video, introducing VC-INR as the first INR-based method to outperform codecs as well-known and diverse as JPEG 2000, MP3 and AVC/HEVC on their respective modalities.

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