**Deformable Position-Sensitive RoI Pooling** is similar to PS RoI Pooling but it adds an offset to each bin position in the regular bin partition. Offset learning follows the “fully convolutional” spirit. In the top branch, a convolutional layer generates the full spatial resolution offset fields. For each RoI (also for each class), PS RoI pooling is applied on such fields to obtain normalized offsets, which are then transformed to the real offsets, in the same way as in deformable RoI pooling.

**MaskFlownet** is an asymmetric occlusion-aware feature matching module, which can learn a rough occlusion mask that filters useless (occluded) areas immediately after feature warping without any explicit supervision. The learned occlusion mask can be further fed into a subsequent network cascade with dual feature pyramids.

MaskFlownet

MaskFlownet: Asymmetric Feature Matching with Learnable Occlusion Mask

Deformable Position-Sensitive RoI Pooling

Deformable Convolutional Networks

**Adversarially Learned Inference (ALI)** is a generative modelling approach that casts the learning of both an inference machine (or encoder) and a deep directed generative model (or decoder) in an GAN-like adversarial framework. A discriminator is trained to discriminate joint samples of the data and the corresponding latent variable from the encoder (or approximate posterior) from joint samples from the decoder while in opposition, the encoder and the decoder are trained together to fool the discriminator. Not is the discriminator asked to distinguish synthetic samples from real data, but it is required it to distinguish between two joint distributions over the data space and the latent variables.

An ALI differs from a [GAN](https://paperswithcode.com/method/gan) in two ways:

- The generator has two components: the encoder, $G\_{z}\left(\mathbf{x}\right)$, which maps data samples $x$ to $z$-space, and the decoder $G\_{x}\left(\mathbf{z}\right)$, which maps samples from the prior $p\left(\mathbf{z}\right)$ (a source of noise) to the input space.
- The discriminator is trained to distinguish between joint pairs $\left(\mathbf{x}, \tilde{\mathbf{z}} = G\_{\mathbf{x}}\left(\mathbf{x}\right)\right)$ and $\left(\tilde{\mathbf{x}} =
G\_{x}\left(\mathbf{z}\right), \mathbf{z}\right)$, as opposed to marginal samples $\mathbf{x} \sim q\left(\mathbf{x}\right)$ and $\tilde{\mathbf{x}} ∼ p\left(\mathbf{x}\right)$.

Source	Deformable Convolutional Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com