COP-KMeans is a modified version the popular k-means algorithm that supports pairwise constraints. 

Original paper : Constrained K-means Clustering with Background Knowledge, Wagstaff et al. 2001

**DV3 Attention Block** is an attention-based module used in the [Deep Voice 3](https://paperswithcode.com/method/deep-voice-3) architecture. It uses a [dot-product attention](https://paperswithcode.com/method/dot-product-attention) mechanism. A query vector (the hidden states of the decoder) and the per-timestep key vectors from the encoder are used to compute attention weights. This then outputs a context vector computed as the weighted average of the value vectors.

DV3 Attention Block

Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning

COP-KMeans

**DetNAS** is a [neural architecture search](https://paperswithcode.com/method/neural-architecture-search) algorithm for the design of better backbones for object detection. It is based on the technique of one-shot supernet, which contains all possible networks in the search space. The supernet is trained under the typical detector training schedule: ImageNet pre-training and detection fine-tuning. Then, the architecture search is performed on the trained supernet, using the detection task as the guidance. DetNAS uses evolutionary search as opposed to RL-based methods or gradient-based methods.

Year	2000
Data Source	CC BY-SA - https://paperswithcode.com