**Darknet-19** is a convolutional neural network that is used as the backbone of [YOLOv2](https://paperswithcode.com/method/yolov2).  Similar to the [VGG](https://paperswithcode.com/method/vgg) models it mostly uses $3 \times 3$ filters and doubles the number of channels after every pooling step. Following the work on Network in Network (NIN) it uses [global average pooling](https://paperswithcode.com/method/global-average-pooling) to make predictions as well as $1 \times 1$ filters to compress the feature representation between $3 \times 3$ convolutions. [Batch Normalization](https://paperswithcode.com/method/batch-normalization) is used to stabilize training, speed up convergence, and regularize the model batch.

**MUSIQ**, or **Multi-scale Image Quality Transformer**, is a [Transformer](https://paperswithcode.com/method/transformer)-based model for multi-scale image quality assessment. It processes native resolution images with varying sizes and aspect ratios. In MUSIQ, we construct a multi-scale image representation as input, including the native resolution image and its ARP resized variants.  Each image is split into fixed-size patches which are embedded by a patch encoding module (blue boxes). To capture 2D structure of the image and handle images of varying aspect ratios, the spatial embedding is encoded by hashing the patch position $(i,j)$ to $(t_{i},t_{j})$ within a grid of learnable embeddings (red boxes). Scale Embedding (green boxes) is introduced to capture scale information. The Transformer encoder takes the input tokens and performs multi-head self-attention. To predict the image quality, MUSIQ follows a common strategy in Transformers to add an [CLS] token to the sequence to represent the whole multi-scale input and the corresponding Transformer output is used as the final representation.

MUSIQ

MUSIQ: Multi-scale Image Quality Transformer

Darknet-19

YOLO9000: Better, Faster, Stronger

**Fraternal Dropout** is a regularization method for recurrent neural networks that trains two identical copies of an RNN (that share parameters) with different [dropout](https://paperswithcode.com/method/dropout) masks while minimizing the difference between their (pre-[softmax](https://paperswithcode.com/method/softmax)) predictions. This encourages the representations of RNNs to be invariant to dropout mask, thus being robust.

Source	YOLO9000: Better, Faster, Stronger
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com