CAE-P: Compressive Autoencoder with Pruning Based on ADMM
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Since compressive autoencoder (CAE) was proposed, autoencoder, as a simple and efficient neural network model, has achieved better performance than traditional codecs such as JPEG[3], JPEG 2000[4] etc. in lossy image compression. However, it faces the problem that the bitrate, characterizing the compression ratio, cannot be optimized by general methods due to its discreteness. Current research additionally trains a entropy estimator to indirectly optimize the bitrate. In this paper, we proposed the compressive autoencoder with pruning based on ADMM (CAE-P) which replaces the traditionally used entropy estimating technique with ADMM pruning method inspired by the field of neural network architecture search and avoided the extra effort needed for training an entropy estimator. We tested our models on natural image dataset Kodak PhotoCD and achieved better results than the original CAE model which relies on entropy coding along with traditional codecs. We further explored the effectiveness of the ADMM-based pruning method in CAE-P by looking into the detail of latent codes learned by the model.
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