**Principal Neighbourhood Aggregation** (PNA) is a general and flexible architecture for graphs combining multiple aggregators with degree-scalers (which generalize the sum aggregator).

**Involution** is an atomic operation for deep neural networks that inverts the design principles of convolution. Involution kernels are distinct in the spatial extent but shared across channels. If involution kernels are parameterized as fixed-sized matrices like convolution kernels and updated using the back-propagation algorithm, the learned involution kernels are impeded from transferring between input images with variable resolutions. 

The authors argue for two benefits of involution over convolution: (i) involution can summarize the context in a wider spatial arrangement, thus overcome the difficulty of modeling long-range interactions well; (ii) involution can adaptively allocate the weights over different positions, so as to prioritize the most informative visual elements in the spatial domain.

Involution

Involution: Inverting the Inherence of Convolution for Visual Recognition

Principal Neighbourhood Aggregation for Graph Nets

Sandwich Batch Normalization (**SaBN**) is a frustratingly easy improvement of [Batch Normalization](https://paperswithcode.com/method/batch-normalization) (BN) with only a few lines of code changes. SaBN is motivated by addressing the inherent *feature distribution heterogeneity* that one can be identified in many tasks, which can arise from data heterogeneity (multiple input domains) or model heterogeneity (dynamic architectures, model conditioning, etc.). Our SaBN factorizes the BN affine layer into one shared *sandwich affine* layer, cascaded by several parallel *independent affine* layers. We demonstrate the prevailing effectiveness of SaBN as a **drop-in replacement in four tasks**: *conditional image generation*, *[neural architecture search](https://paperswithcode.com/method/neural-architecture-search)* (NAS), *adversarial training*, and *arbitrary style transfer*. Leveraging SaBN immediately achieves better Inception Score and FID on CIFAR-10 and ImageNet conditional image generation with three state-of-the-art GANs; boosts the performance of a state-of-the-art weight-sharing NAS algorithm significantly on NAS-Bench-201; substantially improves the robust and standard accuracies for adversarial defense; and produces superior arbitrary stylized results.

Source	Principal Neighbourhood Aggregation for Graph Nets
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com