**Conditional DBlock** is a residual based block used in the discriminator of the [GAN-TTS](https://paperswithcode.com/method/gan-tts) architecture. They are similar to the [GBlocks](https://paperswithcode.com/method/gblock) used in the generator, but without [batch normalization](https://paperswithcode.com/method/batch-normalization). Unlike the [DBlock](https://paperswithcode.com/method/dblock), the Conditional DBlock adds the embedding of the linguistic features after the first [convolution](https://paperswithcode.com/method/convolution).

**U2-Net** is a two-level nested U-structure architecture that is designed for salient object detection (SOD).  The architecture allows the network to go deeper, attain high resolution, without significantly increasing the memory and computation cost. This is achieved by a nested U-structure: on the bottom level, with a novel ReSidual U-block (RSU) module, which is able to extract intra-stage multi-scale features without degrading the feature map resolution; on the top level, there is a [U-Net](https://paperswithcode.com/method/u-net) like structure, in which each stage is filled by a RSU block.

U2-Net

U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection

Conditional DBlock

High Fidelity Speech Synthesis with Adversarial Networks

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.

Source	High Fidelity Speech Synthesis with Adversarial Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com