SCAN automatically groups images into semantically meaningful clusters when ground-truth annotations are absent. SCAN is a two-step approach where feature learning and clustering are decoupled. First, a self-supervised task is employed to obtain semantically meaningful features. Second, the obtained features are used as a prior in a learnable clustering  approach.

Image source: [Gansbeke et al.](https://arxiv.org/pdf/2005.12320v2.pdf)

**SegNet** is a semantic segmentation model. This core trainable segmentation architecture consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. The architecture of the encoder network is topologically identical to the 13 convolutional layers in the
VGG16 network. The role of the decoder network is to map the low resolution encoder feature maps to full input resolution feature maps for pixel-wise classification. The novelty of SegNet lies is in the manner in which the decoder upsamples its lower resolution input feature maps. Specifically, the decoder uses pooling indices computed in the max-pooling step of the corresponding encoder to
perform non-linear upsampling.

SegNet

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

SCAN-clustering

SCAN: Learning to Classify Images without Labels

**PnP**, or **Poll and Pool**, is sampling module extension for [DETR](https://paperswithcode.com/method/detr)-type architectures that adaptively allocates its computation spatially to be more efficient. Concretely, the PnP module abstracts the image feature map into fine foreground object feature vectors and a small number of coarse background contextual feature vectors. The [transformer](https://paperswithcode.com/method/transformer) models information interaction within the fine-coarse feature space and translates the features into the detection result.

Source	SCAN: Learning to Classify Images without Labels
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com