**LiteSeg** is a lightweight architecture for semantic segmentation that uses a deeper version of Atrous [Spatial Pyramid Pooling](https://paperswithcode.com/method/spatial-pyramid-pooling) module ([ASPP](https://paperswithcode.com/method/aspp)) and applies short and long residual connections, and [depthwise separable convolution](https://paperswithcode.com/method/depthwise-separable-convolution), resulting in a faster, more efficient model.

Feature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant features (variables, predictors) for use in model construction.

Feature Selection

Feature Selection and Feature Extraction in Pattern Analysis: A Literature Review

LiteSeg

LiteSeg: A Novel Lightweight ConvNet for Semantic Segmentation

**MoBY** is a self-supervised learning approach for [Vision Transformers](methods/category/vision-transformer). The approach is basically a combination of [MoCo v2](https://paperswithcode.com/method/moco-v2) and [BYOL](https://paperswithcode.com/method/byol). It inherits the momentum design, the key queue, and the contrastive loss used in MoCo v2, and inherits the asymmetric encoders, asymmetric data augmentations and the momentum scheduler in BYOL. It is named MoBY by picking the first two letters of each method.

The MoBY approach is illustrated in the Figure. There are two encoders: an online encoder and a target encoder. Both two encoders consist of a backbone and a projector head ([2-layer MLP](https://paperswithcode.com/method/feedforward-network)), and the online encoder introduces an additional prediction head (2-layer MLP), which makes the two encoders asymmetric. The online encoder is updated by gradients, and the target encoder is a moving average of the online encoder by momentum updating in each training iteration. A gradually increasing momentum updating strategy is applied for on the target encoder: the value of momentum term is gradually increased to 1 during the course of training. The default starting value is $0.99$.

A contrastive loss is applied to learn the representations. Specifically, for an online view $q$, its contrastive loss is computed as

$$
\mathcal{L}\_{q}=-\log \frac{\exp \left(q \cdot k\_{+} / \tau\right)}{\sum\_{i=0}^{K} \exp \left(q \cdot k\_{i} / \tau\right)}
$$

where $k\_{+}$is the target feature for the other view of the same image; $k\_{i}$ is a target feature in the key queue; $\tau$ is a temperature term; $K$ is the size of the key queue (4096 by default).

In training, like most [Transformer-based methods](https://paperswithcode.com/methods/category/transformers), the [AdamW](https://paperswithcode.com/method/adamw) optimizer is used, in contrast to previous [self-supervised learning approaches](https://paperswithcode.com/methods/category/self-supervised-learning) built on [ResNet](https://paperswithcode.com/method/resnet) backbone where usually [SGD](https://paperswithcode.com/method/sgd-with-momentum) or [LARS](https://paperswithcode.com/method/lars) $[4,8,19]$ is used. The authors also use a regularization method of asymmetric [drop path](https://paperswithcode.com/method/droppath) which proves important for the final performance.

In the experiments, the authors adopt a fixed learning rate of $0.001$ and a fixed weight decay of $0.05$, which performs stably well. Hyper-parameters are tuned of the key queue size $K$, the starting momentum value of the target branch, the temperature $\tau$, and the drop path rates.

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