1. Bajaj, K., Singh, D.K., Ansari, M.A.: Autoencoders based deep learner for image denoising. Procedia Comput. Sci. 171, 1535–1541 (2020)
2. Bank, D., Koenigstein, N., Giryes, R.: Autoencoders. arXiv preprint arXiv:2003.05991 (2020)
3. Cao, S., Li, J., Nelson, K.P., Kon, M.A.: Coupled VAE: improved accuracy and robustness of a variational autoencoder. Entropy 24(3), 423 (2022)
4. Cohen, M., Quispe, G., Corff, S.L., Ollion, C., Moulines, E.: Diffusion bridges vector quantized variational autoencoders. In: Chaudhuri, K., Jegelka, S., Song, L., Szepesvari, C., Niu, G., Sabato, S. (eds.) Proceedings of the 39th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 162, pp. 4141–4156. PMLR (2022)
5. El-Shafai, W., et al.: Efficient deep-learning-based autoencoder denoising approach for medical image diagnosis. CMC-Comput. Mater. Continua 70(3), 6107–6125 (2022)