**Iterative Latent Variable Refinement**, or **ILVR**, is a method to guide the generative process in denoising diffusion probabilistic models (DDPMs) to generate high-quality images based on a given reference image. ILVR conditions the generation process in well-performing unconditional DDPM. Each transition in the generation process is refined utilizing a given reference image. By matching each latent variable, ILVR ensures the given condition in each transition thus enables sampling from a conditional distribution. Thus, ILVR generates high-quality images sharing desired semantics.

**ThunderNet** is a two-stage object detection model. The design of ThunderNet aims at the computationally expensive structures in state-of-the-art two-stage detectors. The backbone utilises a [ShuffleNetV2](https://paperswithcode.com/method/shufflenet-v2) inspired network called [SNet](https://paperswithcode.com/method/snet) designed for object detection. In the detection part, ThunderNet follows the detection head design in Light-Head [R-CNN](https://paperswithcode.com/method/r-cnn), and further compresses the [RPN](https://paperswithcode.com/method/rpn) and R-CNN subnet. To eliminate the performance degradation induced by small backbones and small feature maps, ThunderNet uses two new efficient architecture blocks, [Context Enhancement Module](https://paperswithcode.com/method/context-enhancement-module) (CEM) and [Spatial Attention Module](https://paperswithcode.com/method/spatial-attention-module) (SAM). CEM combines the feature maps from multiple scales to leverage local and global context information, while SAM uses the information learned in RPN to refine the feature distribution in RoI warping.

ThunderNet

ThunderNet: Towards Real-time Generic Object Detection

ILVR

ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models

**Negative Face Recognition**, or **NFR**, is a face recognition approach that enhances the soft-biometric privacy on the template-level by representing face templates in a complementary (negative) domain. While ordinary templates characterize facial properties of an individual, negative templates describe facial properties that does not exist for this individual. This suppresses privacy-sensitive information from stored templates. Experiments are conducted on two publicly available datasets captured under controlled and uncontrolled scenarios on three privacy-sensitive attributes.

Source	ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com