End-to-end Learning of Compressible Features

Understand

Pre-trained convolutional neural networks (CNNs) are powerful off-the-shelf feature generators and have been shown to perform very well on a variety of tasks.

• Unfortunately, the generated features are high dimensional and expensive to store: potentially hundreds of thousands of floats per example when processing videos.
• Traditional entropy based lossless compression methods are of little help as they do not yield desired level of compression, while general purpose lossy compression methods based on energy compaction (e.g.
• PCA followed by quantization and entropy coding) are sub-optimal, as they are not tuned to task specific objective.