DASPP is a deeper version of the [ASPP](https://paperswithcode.com/method/aspp) module (the latter from [DeepLabv3](https://paperswithcode.com/method/deeplabv3)) that adds standard 3 × 3 [convolution](https://paperswithcode.com/method/convolution) after 3 × 3 dilated convolutions to refine the features and also fusing the input and the output of the DASPP module via short [residual connection](https://paperswithcode.com/method/residual-connection). Also, the number of convolution filters of ASPP is reduced from 255 to 96 to gain computational performance.

**Automatic Structured Variational Inference (ASVI)** is a fully automated method for constructing structured variational families, inspired by the closed-form update in conjugate Bayesian models. These convex-update families incorporate the forward pass of the input probabilistic program and can therefore capture complex statistical dependencies. Convex-update families have the same space and time complexity as the input probabilistic program and are therefore tractable for a very large family of models including both continuous and discrete variables.

ASVI

Automatic structured variational inference

DASPP

LiteSeg: A Novel Lightweight ConvNet for Semantic Segmentation

**CSPDenseNet-Elastic** is a convolutional neural network and object detection backbone where we apply the Cross Stage Partial Network (CSPNet) approach to [DenseNet-Elastic](https://paperswithcode.com/method/densenet-elastic). The CSPNet partitions the feature map of the base layer into two parts and then merges them through a cross-stage hierarchy. The use of a split and merge strategy allows for more gradient flow through the network.

Source	LiteSeg: A Novel Lightweight ConvNet for Semantic Segmentation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com