**Differential Diffusion** is an enhancement of image-to-image diffusion models that adds the ability to control the amount of change applied to each image fragment via a change map.

**SpecGAN** is a generative adversarial network method for spectrogram-based, frequency-domain audio generation. The problem is suited for GANs designed for image generation. The model can be approximately inverted. 

To process audio into suitable spectrograms, the authors perform the short-time Fourier transform with 16 ms windows and 8ms stride, resulting in 128 frequency bins, linearly spaced from 0 to 8 kHz. They take the magnitude of the resultant spectra and scale amplitude values logarithmically to better-align with human perception. They then normalize each frequency bin to have zero mean and unit variance. They clip the spectra to $3$ standard deviations and rescale to $\left[−1, 1\right]$.

They then use the [DCGAN](https://paperswithcode.com/method/dcgan) approach on the result spectra.

SpecGAN

Adversarial Audio Synthesis

Differential Diffusion

Differential Diffusion: Giving Each Pixel Its Strength

A **Contractive Autoencoder** is an autoencoder that adds a penalty term to the classical reconstruction cost function. This penalty term corresponds to the Frobenius norm of the Jacobian matrix of the encoder activations with respect to the input. This penalty term results in a localized space contraction which in turn yields robust features on the activation layer. The penalty helps to carve a representation that better captures the local directions of variation dictated by the data, corresponding to a lower-dimensional non-linear manifold, while being more invariant to the vast majority of directions orthogonal to the manifold.

Source	Differential Diffusion: Giving Each Pixel Its Strength
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com