**PatchGAN** is a type of discriminator for generative adversarial networks which only penalizes structure at the scale of local image patches. The PatchGAN discriminator tries to classify if each $N \times N$ patch in an image is real or fake. This discriminator is run convolutionally across the image, averaging all responses to provide the ultimate output of $D$. Such a discriminator effectively models the image as a Markov random field, assuming independence between pixels separated by more than a patch diameter. It can be understood as a type of texture/style loss.

**K3M** is a multi-modal pretraining method for e-commerce product data that introduces knowledge modality to correct the noise and supplement the missing of image and text modalities. The modal-encoding layer extracts the features of each modality. The modal-interaction layer is capable of effectively modeling the interaction of multiple modalities, where an initial-interactive feature fusion model is designed to maintain the independence of image modality and text modality, and a structure aggregation module is designed to fuse the information of image, text, and knowledge modalities. K3M is pre-trained with three pretraining tasks, including masked object modeling (MOM), masked language modeling (MLM), and link prediction modeling ([LPM](https://paperswithcode.com/method/local-prior-matching)).

Knowledge Perceived Multi-modal Pretraining in E-commerce

PatchGAN

Image-to-Image Translation with Conditional Adversarial Networks

**ReInfoSelect** is a reinforcement weak supervision selection method for information retrieval. It learns to select anchor-document pairs that best weakly supervise the neural ranker (action), using the ranking performance on a handful of relevance labels as the reward. Iteratively, for a batch of anchor-document pairs, ReInfoSelect back propagates the gradients through the neural ranker, gathers its NDCG reward, and optimizes the data selection network using policy gradients, until the neural ranker's performance peaks on target relevance metrics (convergence).

Source	Image-to-Image Translation with Conditional Adversarial Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com