**Dilated Bottleneck Block** is an image model block used in the [DetNet](https://paperswithcode.com/method/detnet) convolutional neural network architecture. It employs a bottleneck structure with dilated convolutions to efficiently enlarge the receptive field.

GLIDE is a generative model based on text-guided diffusion models for more photorealistic image generation. Guided diffusion is applied to text-conditional image synthesis and the model is able to handle free-form prompts. The diffusion model uses a text encoder to condition on natural language descriptions. The model is provided with editing capabilities in addition to zero-shot generation, allowing for iterative improvement of model samples to match more complex prompts. The model is fine-tuned to perform image inpainting.

GLIDE

GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

Dilated Bottleneck Block

DetNet: A Backbone network for Object Detection

A **BiFPN**, or **Weighted Bi-directional Feature Pyramid Network**, is a type of feature pyramid network which allows easy and fast multi-scale feature fusion. It incorporates the multi-level feature fusion idea from [FPN](https://paperswithcode.com/method/fpn), [PANet](https://paperswithcode.com/method/panet) and [NAS-FPN](https://paperswithcode.com/method/nas-fpn) that enables information to flow in both the top-down and bottom-up directions, while using regular and efficient connections. It also utilizes a fast normalized fusion technique. Traditional approaches usually treat all features input to the FPN equally, even those with different resolutions. However, input features at different resolutions often have unequal contributions to the output features. Thus, the BiFPN adds an additional weight for each input feature allowing the network to learn the importance of each. All regular convolutions are also replaced with less expensive depthwise separable convolutions.

Comparing with PANet, PANet added an extra bottom-up path for information flow at the expense of more computational cost. Whereas BiFPN optimizes these cross-scale connections by removing nodes with a single input edge, adding an extra edge from the original input to output node if they are on the same level, and treating each bidirectional path as one feature network layer (repeating it several times for more high-level future fusion).

Source	DetNet: A Backbone network for Object Detection
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com