**Dilated Bottleneck with Projection 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. It uses a [1x1 convolution](https://paperswithcode.com/method/1x1-convolution) to ensure the spatial size stays fixed.

Understanding and explaining deep learning models is an imperative task. Towards this, we propose a method that obtains gradient-based certainty estimates that also provide [visual attention](https://paperswithcode.com/method/visual-attention) maps. Particularly, we solve for visual question answering task. We incorporate modern probabilistic deep learning methods that we further improve by using the gradients for these estimates. These have two-fold benefits: a) improvement in obtaining the certainty estimates that correlate better with misclassified samples and b) improved attention maps that provide state-of-the-art results in terms of correlation with human attention regions. The improved attention maps result in consistent improvement for various methods for visual question answering. Therefore, the proposed technique can be thought of as a tool for obtaining improved certainty estimates and explanations for deep learning models.

U-CAM

Dilated Bottleneck with Projection Block

DetNet: A Backbone network for Object Detection

**SqueezeNeXt** is a type of convolutional neural network that uses the [SqueezeNet](https://paperswithcode.com/method/squeezenet) architecture as a baseline, but makes a number of changes. First, a more aggressive channel reduction is used by incorporating a two-stage squeeze module. This significantly reduces the total number of parameters used with the 3×3 convolutions. Secondly, it uses separable 3 × 3 convolutions to further reduce the model size, and removes the additional 1×1 branch after the squeeze module. Thirdly, the network use an element-wise addition skip connection similar to that of [ResNet](https://paperswithcode.com/method/resnet) architecture.

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