Discriminative model: The conditional probability of class label on data (posterior) $p(C_k\mid x)$ …
Contrastive models learn to compare1. Contrastive use special objective functions such as NCE Noise …
The task of GAN is to generate features $X$ from some noise $\xi$ and class labels $Y$, $$\xi, Y \to …
In contrastive methods, we can manipulate the data to create data entries and infer the changes …
The essence of GAN GAN The task of GAN is to generate features $X$ from some noise $\xi$ and class …
The likelihood is modeled as $$ \begin{align} p_\theta (x) &= \Pi_{t=1}^T p_\theta (x_t \mid …
It was discovered that the success of mutual information based contrastive learning Contrastive …
For a probability density $p(x)$ and a transformation of coordinate $x=g(z)$ or $z=f(x)$, the …
The simplest auto-encoder is rather simple. The loss can be chosen based on the demand, e.g., cross …
Variational Auto-Encoder (VAE) is very different from Generative Model: Auto-Encoder Generative …
Generative self-supervised learning models can utilize more data
Contrastive self-supervised learning models can utilize more data
Adversarial models use a generator and discriminator
Review of self-supervised learning.