# Cascade Decoders-Based Autoencoders for Image Reconstruction

arXiv: https://arxiv.org/abs/2107.00002 · HTML: https://mlsanity.com/paper/2107.00002/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 27 references that are in the map.
- First posted on arXiv 29 Jun 2021.

## Builds on (most cited first, 6 of 6)

- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [DVC: An End-to-end Deep Video Compression Framework](https://mlsanity.com/paper/1812.00101/) (2018)
- [Variable Rate Image Compression with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06085/) (2015)
- [Blind Denoising Autoencoder](https://mlsanity.com/paper/1912.07358/) (2019)
- [Semi-supervised Stacked Label Consistent Autoencoder for Reconstruction and Analysis of Biomedical Signals](https://mlsanity.com/paper/1912.12127/) (2019)
- [Deep Compressive Autoencoder for Action Potential Compression in Large-Scale Neural Recording](https://mlsanity.com/paper/1809.05522/) (2018)

## Abstract (arXiv)

Autoencoders are composed of coding and decoding units, hence they hold the inherent potential of high-performance data compression and signal compressed sensing. The main disadvantages of current autoencoders comprise the following several aspects: the research objective is not data reconstruction but feature representation; the performance evaluation of data recovery is neglected; it is hard to achieve lossless data reconstruction by pure autoencoders, even by pure deep learning. This paper aims for image reconstruction of autoencoders, employs cascade decoders-based autoencoders, perfects the performance of image reconstruction, approaches gradually lossless image recovery, and provides solid theory and application basis for autoencoders-based image compression and compressed sensing. The proposed serial decoders-based autoencoders include the architectures of multi-level decoders and the related optimization algorithms. The cascade decoders consist of general decoders, residual decoders, adversarial decoders and their combinations. It is evaluated by the experimental results that the proposed autoencoders outperform the classical autoencoders in the performance of image reconstruction.