**SNIPER** is a multi-scale training approach for instance-level recognition tasks like object detection and instance-level segmentation. Instead of processing all pixels in an image pyramid, SNIPER selectively processes context regions around the ground-truth objects (a.k.a chips). This can help to speed up multi-scale training as it operates on low-resolution chips. Due to its memory-efficient design, SNIPER can benefit from [Batch Normalization](https://paperswithcode.com/method/batch-normalization) during training and it makes larger batch-sizes possible for instance-level recognition tasks on a single GPU.

**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).

ClariNet

ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech

SNIPER

SNIPER: Efficient Multi-Scale Training

The **Invertible 1x1 Convolution** is a type of [convolution](https://paperswithcode.com/method/convolution) used in flow-based generative models that reverses the ordering of channels. The weight matrix is initialized as a random rotation matrix. The log-determinant of an invertible 1 × 1 convolution of a $h \times w \times c$ tensor $h$ with $c \times c$ weight matrix $\mathbf{W}$ is straightforward to compute:

$$ \log | \text{det}\left(\frac{d\text{conv2D}\left(\mathbf{h};\mathbf{W}\right)}{d\mathbf{h}}\right) | = h \cdot w \cdot \log | \text{det}\left(\mathbf{W}\right) | $$

Source	SNIPER: Efficient Multi-Scale Training
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com