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Papers/Lossy Compression for Lossless Prediction

Lossy Compression for Lossless Prediction

Yann Dubois, Benjamin Bloem-Reddy, Karen Ullrich, Chris J. Maddison

2021-06-21NeurIPS 2021 12PredictionImage Compression
PaperPDFCode(official)

Abstract

Most data is automatically collected and only ever "seen" by algorithms. Yet, data compressors preserve perceptual fidelity rather than just the information needed by algorithms performing downstream tasks. In this paper, we characterize the bit-rate required to ensure high performance on all predictive tasks that are invariant under a set of transformations, such as data augmentations. Based on our theory, we design unsupervised objectives for training neural compressors. Using these objectives, we train a generic image compressor that achieves substantial rate savings (more than $1000\times$ on ImageNet) compared to JPEG on 8 datasets, without decreasing downstream classification performance.

Results

TaskDatasetMetricValueModel
Image CompressionCaltech101Bit rate1340Lossyless Compressor
Image CompressionCIFAR-10Bit rate1410Lossyless Compressor
Image CompressionCars-196Bit rate1470Lossyless Compressor
Image CompressionFood-101Bit rate1270Lossyless Compressor
Image CompressionPCamBit rate1490Lossyless Compressor
Image CompressionOxford-IIIT Pet DatasetBit rate1210Lossyless Compressor
Image CompressionSTL-10Bit rate1340Lossyless Compressor

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