**Colorization** is a self-supervision approach that relies on colorization as the pretext task in order to learn image representations.

**Attribute2Font** is a model that automatically creates fonts by synthesizing visually pleasing glyph images according to user-specified attributes and their corresponding values. Specifically, Attribute2Font is trained to perform font style transfer between any two fonts conditioned on their attribute values. After training, the model can generate glyph images in accordance with an arbitrary set of font attribute values. A unit named Attribute Attention Module is designed to make those generated glyph images better embody the prominent font attributes. A semi-supervised learning scheme is also introduced to exploit a large number of unlabeled fonts

Attribute2Font

Attribute2Font: Creating Fonts You Want From Attributes

Colorization

Colorful Image Colorization

A **Multiplicative RNN (mRNN)** is a type of recurrent neural network with multiplicative connections. In a standard RNN, the current input $x\_{t}$ is first transformed via the visible-to-hidden weight matrix $W\_{hx}$ and then contributes additively to the input for the current hidden state. An mRNN allows the current input (a character in the original example) to affect the hidden state dynamics by determining the entire hidden-to-hidden matrix (which defines the non-linear dynamics) in addition to providing an additive bias.

To achieve this goal, the authors modify the RNN so that its hidden-to-hidden weight matrix is a (learned) function of the current input $x\_{t}$:

$$ h\_{t} = \tanh\left(W\_{hx}x\_{t} + W\_{hh}^{\left(x\_{y}\right)}h\_{t-1} + b\_{h}\right)$$

$$ o\_{t} = W\_{oh}h\_{t} + b\_{o} $$

This is the same as the equations for a standard RNN, except that $W\_{hh}$ is replaced with $W^{(xt)}\_{hh}$. allowing each input (character) to specify a different hidden-to-hidden weight matrix.

Source	Colorful Image Colorization
Year	2000
Data Source	Papers with Code archive (CC BY-SA)