F2DNet, a novel two-stage object detection architecture which eliminates redundancy of classical two-stage detectors by replacing the region proposal network with focal detection network and
bounding box head with fast suppression head.

**GBlock** is a type of [residual block](https://paperswithcode.com/method/residual-block) used in the [GAN-TTS](https://paperswithcode.com/method/gan-tts) text-to-speech architecture - it is a stack of two residual blocks. As the generator is producing raw audio (e.g. a 2s training clip corresponds
to a sequence of 48000 samples), dilated convolutions are used to ensure that the receptive field of $G$ is large enough to capture long-term dependencies. The four kernel size-3 convolutions in each GBlock have increasing dilation factors: 1, 2, 4, 8. Convolutions are preceded by Conditional Batch Normalisation, conditioned on the linear embeddings of the noise term $z \sim N\left(0, \mathbf{I}\_{128}\right)$ in the single-speaker case, or the concatenation of $z$ and a one-hot representation of the speaker ID in the multi-speaker case. The embeddings are different for
each BatchNorm instance. 

A GBlock contains two skip connections, the first of which in [GAN](https://paperswithcode.com/method/gan)-TTS performs upsampling if the output frequency is higher than the input, and it also contains a size-1 [convolution](https://paperswithcode.com/method/convolution)
if the number of output channels is different from the input.

GBlock

High Fidelity Speech Synthesis with Adversarial Networks

F2DNet

F2DNet: Fast Focal Detection Network for Pedestrian Detection

**DenseNAS-A** is a mobile convolutional neural network discovered through the [DenseNAS](https://paperswithcode.com/method/densenas) [neural architecture search](https://paperswithcode.com/method/neural-architecture-search) method. The basic building block is MBConvs, or inverted bottleneck residuals, from the MobileNet architectures.

Source	F2DNet: Fast Focal Detection Network for Pedestrian Detection
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com