Undercomplete Autoencoders

• Geoffrey E Hinton and Richard S Zemel. Autoencoders, minimum description length, and helmholtz free energy. In NeurIPS, 1994.

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Sparse Autoencoders

Contractive Autoencoders

Denoising Autoencoders

• Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and PierreAntoine Manzagol. Extracting and composing robust features with denoising autoencoders. In ICML, 2008.

• Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, Pierre-Antoine Manzagol, and Leon Bottou. "Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion", in JMLR, 2010.

• Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A Efros,  "Context encoders: Feature learning by inpainting", in CVPR, 2016.

• Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, and Ilya Sutskever, "Generative pre-training from pixels", in ICML, 2020.

Variational Autoencoder

• Finetuning Pretrained Transformers into Variational Autoencoders_2021 [Paper]

• Rethinking Coarse-to-Fine Approach in Single Image Deblurring [Paper]

• T. Cemgil, S. Ghaisas, K. Dvijotham, S. Gowal, P. Kohli, "The Autoencoding Variational Autoencoder", in Part of Advances in Neural Information Processing Systems 33 (NeurIPS), 2020.

• [VRAE] O. Fabius, J. R. V. Amersfoort, "Variational Recurrent Auto-Encoders", in ICLRw 2015 [Code]

• [NCP-VAE] J Aneja, A Schwing, J Kautz, A Vahdat, "NCP-VAE: Variational autoencoders with noise contrastive priors", Under Review at ICLR 2021, 2021.

• Coupled VAE: Improved Accuracy and Robustness of a Variational Autoencoder.

• InfoVAE: Information Maximizing Variational Autoencoders

Adversarial Autoencoder

• • [AAE] A. Makhzani, J. Shlens, N. Jaitly, I. Goodfellow, and B. Frey, "Adversarial Autoencoders", in arXiv preprint arXiv:1511.05644, 2015

  • [ALAE] S. Pidhorskyi, D. Adjeroh, G. Doretto, "Adversarial Latent Autoencoders", in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14104-14113, 2020. [Code]

Masked Autoencoders

• • [MAE] K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and  R. Girshick, "Masked Autoencoders Are Scalable Vision Learners", in arXiv preprint arXiv:2111.06377, 2021.

  • [MAEv2] C. Feichtenhofer, H. Fan, Y. Li, and K. He, "Masked Autoencoders As Spatiotemporal Learners", arXiv preprint arXiv:2205.09113, 2022.

Swapping Autoencoder

• • T. Park, J. Y. Zhu, O. Wang, J. Lu, E. Shechtman, A. Efros, and R. Zhang, "Swapping Autoencoder for Deep Image Manipulation", Advances in Neural Information Processing Systems 33 (NeurIPS), 2020.

• Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation [Link] [Code] [Colab]

• ViTGAN: Training GANs with Vision Transformers_2021 [Paper]

• Drafting and Revision: Laplacian Pyramid Network for Fast High-Quality Artistic Style Transfer_CVPR_2021 [Paper]

• Online Multi-Granularity Distillation for GAN Compression [Paper]

• SofGAN: A Portrait Image Generator with Dynamic Styling [Paper]

• Adaptable GAN Encoders for Image Reconstruction via Multi-type Latent Vectors with Two-scale Attentions [Paper]

• Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data [Paper]

• Liquid Warping GAN with Attention: A Unified Framework for Human Image Synthesis [Paper]

• Ensembling Off-the-shelf Models for GAN Training [Paper] [Code] [QuickRead] [Video]

Super Resolution

• [SR-GAN] "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network"

Medical Image

• "ToStaGAN: An end-to-end two-stage generative adversarial network for brain tumor segmentation"

Unfilter:

• • What Regularized Auto-Encoders Learn from the Data Generating Distribution [Paper]

  • Neural Networks, Manifolds, and Topology [Link]

  • Kullback-Leibler Divergence Explained [Link]