FixMatch is an algorithm that first generates pseudo-labels using the model's predictions on weakly-augmented unlabeled images. For a given image, the pseudo-label is only retained if the model produces a high-confidence prediction. The model is then trained to predict the pseudo-label when fed a strongly-augmented version of the same image.

Description from: [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://paperswithcode.com/paper/fixmatch-simplifying-semi-supervised-learning)

Image credit:  [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://paperswithcode.com/paper/fixmatch-simplifying-semi-supervised-learning)

**Twins-PCPVT** is a type of [vision transformer](https://paperswithcode.com/methods/category/vision-transformer) that combines global attention, specifically the global sub-sampled attention as proposed in [Pyramid Vision Transformer](https://paperswithcode.com/method/pvt), with [conditional position encodings](https://paperswithcode.com/method/conditional-positional-encoding) (CPE) to replace the [absolute position encodings](https://paperswithcode.com/method/absolute-position-encodings) used in PVT.

The [position encoding generator](https://paperswithcode.com/method/positional-encoding-generator) (PEG), which generates the CPE, is placed after the first encoder block of each stage. The simplest form of PEG is used, i.e., a 2D [depth-wise convolution](https://paperswithcode.com/method/depthwise-convolution) without [batch normalization](https://paperswithcode.com/method/batch-normalization). For image-level classification, following [CPVT](https://paperswithcode.com/method/cpvt), the class token is removed and [global average pooling](https://paperswithcode.com/method/global-average-pooling) is used at the end of the stage. For other vision tasks, the design of PVT is followed.

Twins-PCPVT

Twins: Revisiting the Design of Spatial Attention in Vision Transformers

FixMatch

FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence

**Bottom-up Path Augmentation** is a feature extraction technique that seeks to shorten the information path and enhance a feature pyramid with accurate localization signals existing in low-levels. This is based on the fact that high response to edges or instance parts is a strong indicator to accurately localize instances. 

Each building block takes a higher resolution feature map $N\_{i}$ and a coarser map $P\_{i+1}$ through lateral connection and generates the new feature map $N\_{i+1}$ Each feature map $N\_{i}$ first goes through a $3 \times 3$ convolutional layer with stride $2$ to reduce the spatial size. Then each element of feature map $P\_{i+1}$ and the down-sampled map are added through lateral connection. The fused feature map is then processed by another $3 \times 3$ convolutional layer to generate $N\_{i+1}$ for following sub-networks. This is an iterative process and terminates after approaching $P\_{5}$. In these building blocks, we consistently use channel 256 of feature maps. The feature grid for each proposal is then pooled from new feature maps, i.e., {$N\_{2}$, $N\_{3}$, $N\_{4}$, $N\_{5}$}.

Source	FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com