GAN Least Squares Loss is a least squares loss function for generative adversarial networks. Minimizing this objective function is equivalent to minimizing the Pearson $\chi^{2}$ divergence. The objective function (here for LSGAN) can be defined as:
$$ \min_{D}V_{LS}\left(D\right) = \frac{1}{2}\mathbb{E}_{\mathbf{x} \sim p_{data}\left(\mathbf{x}\right)}\left[\left(D\left(\mathbf{x}\right)  b\right)^{2}\right] + \frac{1}{2}\mathbb{E}_{\mathbf{z}\sim p_{data}\left(\mathbf{z}\right)}\left[\left(D\left(G\left(\mathbf{z}\right)\right)  a\right)^{2}\right] $$
$$ \min_{G}V_{LS}\left(G\right) = \frac{1}{2}\mathbb{E}_{\mathbf{z} \sim p_{\mathbf{z}}\left(\mathbf{z}\right)}\left[\left(D\left(G\left(\mathbf{z}\right)\right)  c\right)^{2}\right] $$
where $a$ and $b$ are the labels for fake data and real data and $c$ denotes the value that $G$ wants $D$ to believe for fake data.
Source: Least Squares Generative Adversarial NetworksPaper  Code  Results  Date  Stars 

Task  Papers  Share 

ImagetoImage Translation  58  15.43% 
Domain Adaptation  27  7.18% 
Image Generation  27  7.18% 
Semantic Segmentation  19  5.05% 
Style Transfer  16  4.26% 
Unsupervised Domain Adaptation  10  2.66% 
Voice Conversion  10  2.66% 
SuperResolution  9  2.39% 
Unsupervised ImageToImage Translation  9  2.39% 
Component  Type 


🤖 No Components Found  You can add them if they exist; e.g. Mask RCNN uses RoIAlign 