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.

SRM combines style transfer with an attention mechanism. Its main contribution is style pooling which utilizes both mean and standard deviation of the input features to improve its capability to capture global information. It also adopts a lightweight channel-wise fully-connected (CFC) layer, in place of the original fully-connected layer, to reduce the computational requirements.
Given an input feature map $X \in \mathbb{R}^{C \times H \times W}$, SRM first collects global information by using style pooling ($\text{SP}(\cdot)$) which combines global average pooling and global standard deviation pooling. 
Then a channel-wise fully connected ($\text{CFC}(\cdot)$) layer (i.e. fully connected per channel), batch normalization $\text{BN}$ and sigmoid function $\sigma$ are used  to provide the attention vector. Finally,   as in an SE block, the input features are multiplied by the attention vector. Overall, an SRM can be written as:
\begin{align}
    s = F_\text{srm}(X, \theta) & = \sigma (\text{BN}(\text{CFC}(\text{SP}(X))))
\end{align}
\begin{align}
    Y & = s  X
\end{align}
The SRM block improves both squeeze and excitation modules, yet can be added after each residual unit like an SE block.

SRM: A Style-Based Recalibration Module for Convolutional Neural Networks

Object Dropout

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

**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)

Source	Perturb, Predict & Paraphrase: Semi-Supervised Learning using Noisy Student for Image Captioning
Year	2000
Data Source	Papers with Code archive (CC BY-SA)