**Adversarial Color Enhancement** is an approach to generating unrestricted adversarial images by optimizing a color filter via gradient descent.

VLG-Net leverages recent advantages in Graph Neural Networks (GCNs) and leverages a novel multi-modality graph-based fusion method for the task of natural language video grounding.

VLG-Net

VLG-Net: Video-Language Graph Matching Network for Video Grounding

Adversarial Color Enhancement

Adversarial Color Enhancement: Generating Unrestricted Adversarial Images by Optimizing a Color Filter

**Group Decreasing Network**, or **GroupDNet**, is a type of convolutional neural network for multi-modal image synthesis. GroupDNet contains one encoder and one decoder. Inspired by the idea of [VAE](https://paperswithcode.com/method/vae) and SPADE, the encoder $E$ produces a
latent code $Z$ that is supposed to follow a Gaussian distribution $\mathcal{N}(0,1)$ during training. While testing, the encoder $E$ is discarded. A randomly sampled code from the Gaussian distribution substitutes for $Z$. To fulfill this, the re-parameterization trick is used to enable a differentiable loss function during training. Specifically, the encoder predicts a mean vector and a variance vector through two fully connected layers to represent the encoded distribution. The gap between the encoded distribution and Gaussian distribution can be minimized by imposing a KL-divergence loss.

Source	Adversarial Color Enhancement: Generating Unrestricted Adversarial Images by Optimizing a Color Filter
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com