**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.

**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.

DVD-GAN GBlock

Adversarial Video Generation on Complex Datasets

DAPO

Dialogue-adaptive Language Model Pre-training From Quality Estimation

**BRepNet** is a neural network for CAD applications. It is designed to operate directly on B-rep data structures, avoiding the need to approximate the model as meshes or point clouds. BRepNet defines convolutional kernels with respect to oriented coedges in the data structure. In the neighborhood of each coedge, a small collection of faces, edges and coedges can be identified and patterns in the feature vectors from these entities detected by specific learnable parameters.

Source	Dialogue-adaptive Language Model Pre-training From Quality Estimation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com