**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).

**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.

PatchGAN

Image-to-Image Translation with Conditional Adversarial Networks

ReInfoSelect

Selective Weak Supervision for Neural Information Retrieval

A **Fire Module** is a building block for convolutional neural networks, notably used as part of [SqueezeNet](https://paperswithcode.com/method/squeezenet). A Fire module is comprised of: a squeeze [convolution](https://paperswithcode.com/method/convolution) layer (which has only 1x1 filters), feeding into an expand layer that has a mix of 1x1 and 3x3 convolution filters.  We expose three tunable dimensions (hyperparameters) in a Fire module: $s\_{1x1}$, $e\_{1x1}$, and $e\_{3x3}$. In a Fire module, $s\_{1x1}$ is the number of filters in the squeeze layer (all 1x1), $e\_{1x1}$ is the number of 1x1 filters in the expand layer, and $e\_{3x3}$ is the number of 3x3 filters in the expand layer. When we use Fire modules we set $s\_{1x1}$ to be less than ($e\_{1x1}$ + $e\_{3x3}$), so the squeeze layer helps to limit the number of input channels to the 3x3 filters.

Source	Selective Weak Supervision for Neural Information Retrieval
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com