**DVD-GAN GBlock** is a [residual block](https://paperswithcode.com/method/residual-block) for the generator used in the [DVD-GAN](https://paperswithcode.com/method/dvd-gan) architecture for video generation.

**Embedded Gaussian Affinity** is a type of affinity or self-similarity function between two points $\mathbf{x\_{i}}$ and $\mathbf{x\_{j}}$ that uses a Gaussian function in an embedding space:

$$ f\left(\mathbf{x\_{i}}, \mathbf{x\_{j}}\right) = e^{\theta\left(\mathbf{x\_{i}}\right)^{T}\phi\left(\mathbf{x\_{j}}\right)} $$

Here $\theta\left(x\_{i}\right) = W\_{θ}x\_{i}$ and $\phi\left(x\_{j}\right) = W\_{φ}x\_{j}$ are two embeddings.

Note that the self-attention module used in the original [Transformer](https://paperswithcode.com/method/transformer) model is a special case of non-local operations in the embedded Gaussian version. This can be seen from the fact that for a given $i$, $\frac{1}{\mathcal{C}\left(\mathbf{x}\right)}\sum\_{\forall{j}}f\left(\mathbf{x}\_{i}, \mathbf{x}\_{j}\right)g\left(\mathbf{x}\_{j}\right)$ becomes the [softmax](https://paperswithcode.com/method/softmax) computation along the dimension $j$. So we have $\mathbf{y} = \text{softmax}\left(\mathbf{x}^{T}W^{T}\_{\theta}W\_{\phi}\mathbf{x}\right)g\left(\mathbf{x}\right)$, which is the self-attention form in the Transformer model. This shows how we can relate this recent self-attention model to the classic computer vision method of non-local means.

Embedded Gaussian Affinity

Non-local Neural Networks

DVD-GAN GBlock

Adversarial Video Generation on Complex Datasets

**Dialogue-Adaptive Pre-training Objective (DAPO)** is a pre-training objective for dialogue adaptation, which is designed to measure qualities of dialogues from multiple important aspects, like Readability, Consistency and Fluency which have already been focused on by general LM pre-training objectives, and those also significant for assessing dialogues but ignored by general LM pre-training objectives, like Diversity and Specificity.

Source	Adversarial Video Generation on Complex Datasets
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com