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:

$$ \log | \text{det}\left(\frac{d\text{conv2D}\left(\mathbf{h};\mathbf{W}\right)}{d\mathbf{h}}\right) | = h \cdot w \cdot \log | \text{det}\left(\mathbf{W}\right) | $$

**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.

SNIPER

SNIPER: Efficient Multi-Scale Training

Invertible 1x1 Convolution

Glow: Generative Flow with Invertible 1x1 Convolutions

**CRF-RNN** is a formulation of a [CRF](https://paperswithcode.com/method/crf) as a Recurrent Neural Network. Specifically it formulates mean-field approximate inference for the Conditional Random Fields with Gaussian pairwise potentials as Recurrent Neural Networks.

Source	Glow: Generative Flow with Invertible 1x1 Convolutions
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com