**COLA** is a self-supervised pre-training approach for learning a general-purpose representation of audio. It is based on contrastive learning: it learns a representation which assigns high similarity to audio segments extracted from the same recording while assigning lower similarity to segments from different recordings.

**Stable Rank Normalization (SRN)** is a weight-normalization scheme which minimizes the
stable rank of a linear operator. It simultaneously controls the Lipschitz constant and the stable rank of a linear operator. Stable rank is a softer version of the rank operator and is defined as the squared ratio of the Frobenius norm to the spectral norm.

Stable Rank Normalization for Improved Generalization in Neural Networks and GANs

COLA

Contrastive Learning of General-Purpose Audio Representations

**DeepLabv3** is a semantic segmentation architecture that improves upon [DeepLabv2](https://paperswithcode.com/method/deeplabv2) with several modifications. To handle the problem of segmenting objects at multiple scales, modules are designed which employ atrous [convolution](https://paperswithcode.com/method/convolution) in cascade or in parallel to capture multi-scale context by adopting multiple atrous rates. Furthermore, the Atrous [Spatial Pyramid Pooling](https://paperswithcode.com/method/spatial-pyramid-pooling) module from DeepLabv2 augmented with image-level features encoding global context and further boost performance. 

The changes to the ASSP module are that the authors apply [global average pooling](https://paperswithcode.com/method/global-average-pooling) on the last feature map of the model, feed the resulting image-level features to a 1 × 1 convolution with 256 filters (and [batch normalization](https://paperswithcode.com/method/batch-normalization)), and then bilinearly upsample the feature to the desired spatial dimension. In the
end, the improved [ASPP](https://paperswithcode.com/method/aspp) consists of (a) one 1×1 convolution and three 3 × 3 convolutions with rates = (6, 12, 18) when output stride = 16 (all with 256 filters and batch normalization), and (b) the image-level features.

Another interesting difference is that DenseCRF post-processing from DeepLabv2 is no longer needed.

Source	Contrastive Learning of General-Purpose Audio Representations
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com