**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.

HBMP is a hierarchy-like structure of [BiLSTM](https://paperswithcode.com/method/bilstm) layers with [max pooling](https://paperswithcode.com/method/max-pooling). All in all, this model improves the previous state of the art for SciTail and achieves strong results for the SNLI and MultiNLI.

HBMP

Sentence Embeddings in NLI with Iterative Refinement Encoders

LiteSeg

LiteSeg: A Novel Lightweight ConvNet for Semantic Segmentation

BYOL (Bootstrap Your Own Latent) is a new approach to self-supervised learning. BYOL’s goal is to learn a representation $y_θ$ which can then be used for downstream tasks. BYOL uses two neural networks to learn: the online and target networks. The online network is defined by a set of weights $θ$ and is comprised of three stages: an encoder $f_θ$, a projector $g_θ$ and a predictor $q_θ$. The target network has the same architecture
as the online network, but uses a different set of weights $ξ$. The target network provides the regression
targets to train the online network, and its parameters $ξ$ are an exponential moving average of the
online parameters $θ$.

Given the architecture diagram on the right, BYOL minimizes a similarity loss between $q_θ(z_θ)$ and $sg(z'{_ξ})$, where $θ$ are the trained weights, $ξ$ are an exponential moving average of $θ$ and $sg$ means stop-gradient. At the end of training, everything but $f_θ$ is discarded, and $y_θ$ is used as the image representation.

Source: [Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning](https://paperswithcode.com/paper/bootstrap-your-own-latent-a-new-approach-to-1)

Image credit: [Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning](https://paperswithcode.com/paper/bootstrap-your-own-latent-a-new-approach-to-1)

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