**Linear Regression** is a method for modelling a relationship between a dependent variable and independent variables. These models can be fit with numerous approaches. The most common is *least squares*, where we minimize the mean square error between the predicted values $\hat{y} = \textbf{X}\hat{\beta}$ and actual values $y$: $\left(y-\textbf{X}\beta\right)^{2}$.

We can also define the problem in probabilistic terms as a generalized linear model (GLM) where the pdf is a Gaussian distribution, and then perform maximum likelihood estimation to estimate $\hat{\beta}$.

Image Source: [Wikipedia](https://en.wikipedia.org/wiki/Linear_regression)

Object Dropout is a technique that perturbs object features in an image for [noisy student](https://paperswithcode.com/method/noisy-student) training. It performs at par with standard data augmentation techniques while being significantly faster than the latter to implement.

Object Dropout

Perturb, Predict & Paraphrase: Semi-Supervised Learning using Noisy Student for Image Captioning

Linear Regression

**Stand-Alone Self Attention** (SASA) replaces all instances of spatial [convolution](https://paperswithcode.com/method/convolution) with a form of self-attention applied to [ResNet](https://paperswithcode.com/method/resnet) producing a fully, stand-alone self-attentional model.

Year	2000
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