Class activation maps could be used to interpret the prediction decision made by the convolutional neural network (CNN).

Image source: [Learning Deep Features for Discriminative Localization](https://paperswithcode.com/paper/learning-deep-features-for-discriminative)

**Path Aggregation Network**, or **PANet**, aims to boost information flow in a proposal-based instance segmentation framework. Specifically, the feature hierarchy is enhanced with accurate localization signals in lower layers by [bottom-up path augmentation](https://paperswithcode.com/method/bottom-up-path-augmentation), which shortens the information path between lower layers and topmost feature. Additionally, [adaptive feature pooling](https://paperswithcode.com/method/adaptive-feature-pooling) is employed, which links feature grid and all feature levels to make useful information in each feature level propagate directly to following proposal subnetworks. A complementary branch capturing different views for each proposal is created to further improve mask prediction.

PANet

Path Aggregation Network for Instance Segmentation

Is Object Localization for Free? - Weakly-Supervised Learning With Convolutional Neural Networks

**ClariNet** is an end-to-end text-to-speech architecture. Unlike previous TTS systems which use text-to-spectogram models with a separate waveform [synthesizer](https://paperswithcode.com/method/synthesizer) (vocoder), ClariNet is a text-to-wave architecture that is fully convolutional and can be trained from scratch. In ClariNet, the [WaveNet](https://paperswithcode.com/method/wavenet) module is conditioned on the hidden states instead of the mel-spectogram. The architecture is otherwise based on [Deep Voice 3](https://paperswithcode.com/method/deep-voice-3).

Source	Is Object Localization for Free? - Weakly-Supervised Learning With Convolutional Neural Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com