In this story, Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks, (CycleGAN), by Berkeley AI Research (BAIR) laboratory, UC Berkeley, is reviewed.

For many tasks, paired training data will not be available.

In this paper:

• CycleGAN is designed to translate an image from a source domain X to a target domain Y in the absence of paired examples, i.e. G: X→Y.
• This mapping is highly under-constrained, an inverse mapping F: Y→X is coupled and a cycle consistency loss is introduced to enforce F(G(X))=X (and vice versa).

This is a paper in 2017 ICCV with over 8300 citations. (Sik-Ho Tsang @ Medium)

Outline

1. …

In this story, Fast Intra Coding Algorithm for Depth Map with End-to-End Edge Detection Network, (Liu VCIP’20), is briefly reviewed since I need to review a manuscript in one IEEE transactions. In this paper:

• First, Holistically Nested Edge Detection (HED) network is used for edge detection.
• Then, Ostu method is used to divide the output of the HED into foreground region and background region.
• Finally, the CU size and the candidate list of intra mode are determined according to the region of coding tree unit (CTU).

This is a paper in 2020 VCIP. (Sik-Ho Tsang @ Medium)

Outline

1. Brief Introduction in…

In this story, Deep Learning-Based Nonlinear Transform for HEVC Intra Coding, (DLT), is reviewed. In this paper:

• A convolutional neural network (CNN) model is designed as Deep Learning-Based Transform (DLT) to achieve better decorrelation and energy compaction than the conventional discrete cosine transform (DCT).
• The intra prediction signal is utilized as side information to reduce the directionality in the residual.
• A novel loss function is used to characterize the efficiency of the transform during the training.

This is a paper in 2020 VCIP. (Sik-Ho Tsang @ Medium)

Outline

1. Directionality in Residual Domain
2. DLT: Network Architecture
3. Loss Function
4. Experimental Results

1. Directionality in Residual Domain

In this story, Adversarially Learned Inference, (ALI), by Université de Montréal, Stanford, New York University, and CIFAR Fellow, is briefly reviewed. In this story:

• The generation network maps samples from stochastic latent variables to the data space.
• The inference network maps training examples in data space to the space of latent variables.
• The discriminative network is trained to distinguish between joint latent/data-space samples from the generative network and joint samples from the inference network.

This is a paper in 2017 ICLR with over 1000 citations. (Sik-Ho Tsang @ Medium)

The idea is the same as BiGAN, but they are proposed…

In this story, Context Encoding for Semantic Segmentation, (EncNet), by Rutgers University, Amazon Inc, SenseTime, and The Chinese University of Hong Kong, is reviewed. In this paper:

• Context Encoding Module is introduced, which captures the semantic context of scenes and selectively highlights class-dependent feature maps.
• For example in the above figure, the suite room scene will seldom have a horse, but more likely there will be chair, bed and curtain, etc. In this case, this module helps to highlight chair, bed and curtain.

This is a paper in 2018 CVPR with over 500 citations. (Sik-Ho Tsang @ Medium)

Outline

1. Context Encoding…

In this story, Adversarial Feature Learning, (BiGAN), by University of California, and University of Texas, is briefly reviewed. In this paper:

• Bidirectional Generative Adversarial Network (BiGAN) is designed as a means of learning the inverse mapping, i.e. projecting data back into the latent space.
• This resulting learned feature representation is useful for auxiliary supervised discrimination tasks.

This is a paper in 2017 ICLR with over 1100 citations. (Sik-Ho Tsang @ Medium)

The idea is the same as ALI while they are proposed independently and published in the same conference (2017 ICLR). …

In this story, Learning a Discriminative Feature Network for Semantic Segmentation, (DFN), by Huazhong University of Science and Technology, Peking University, and Megvii Inc. (Face++), is reviewed. In this paper:

• Discriminative Feature Network (DFN) has 2 sub-networks.
• One is the Smooth Network, to handle the intra-class inconsistency problem with Channel Attention Block and global average pooling to select the more discriminative features.
• One is the Border Network, to make the bilateral features of boundary distinguishable with deep semantic boundary supervision.

This is a paper in 2018 CVPR with over 360 citations. (Sik-Ho Tsang @ Medium)

Outline

1. DFN: Network Architecture
2. Smooth Network
3. …

In this story, Adversarial Autoencoders, (AAE), by University of Toronto, Google Brain, and OpenAI, is briefly reviewed. Only the AAE variants are described. This is a paper by Ian Goodfellow, who is also the first author of GAN. In this paper:

• AAE is a probabilistic autoencoder that uses GAN.
• The decoder of the adversarial autoencoder learns a deep generative model that maps the imposed prior to the data distribution.

This is a paper in 2016 ICLR with over 1600 citations. (Sik-Ho Tsang @ Medium)

Outline

1. AAE: Network Architecture
2. AAE vs VAE
3. Supervised AAE
4. Semi-supervised AAE
5. Unsupervised AAE
6. Dimension Reduction for Data…