**DropPathway** randomly drops an audio pathway during training as a regularization technique for audiovisual recognition models.  Specifically, at each training iteration, we drop the Audio pathway altogether with probability $P\_{d}$. This way, we slow down the learning of the Audio pathway and make its learning dynamics more compatible with its visual counterpart. When dropping the audio pathway, we sum zero tensors with the visual pathways.

Note that DropPathway is different from simply setting different learning rates for the audio/visual pathways in that it 1) ensures the audio pathway has fewer parameter updates, 2) hinders the visual pathway to 'shortcut' training by memorizing audio information, and 3) provides extra regularization as different audio clips are dropped in each epoch.

**I-BERT** is a quantized version of [BERT](https://paperswithcode.com/method/bert) that quantizes the entire inference with integer-only arithmetic. Based on lightweight integer only approximation methods for nonlinear operations, e.g., [GELU](https://paperswithcode.com/method/gelu), [Softmax](https://paperswithcode.com/method/softmax), and [Layer Normalization](https://paperswithcode.com/method/layer-normalization), it performs an end-to-end integer-only [BERT](https://paperswithcode.com/method/bert) inference without any floating point calculation.

In particular, GELU and Softmax are approximated with lightweight second-order polynomials, which can be evaluated with integer-only arithmetic. For LayerNorm, integer-only computation is performed by leveraging a known algorithm for integer calculation of
square root.

I-BERT

I-BERT: Integer-only BERT Quantization

DropPathway

Audiovisual SlowFast Networks for Video Recognition

**Hi-LANDER** is a hierarchical [graph neural network](https://paperswithcode.com/methods/category/graph-models) (GNN) model that learns how to cluster a set of images into an unknown number of identities using an image annotated with labels belonging to a disjoint set of identities. The hierarchical GNN uses an approach to merge connected components predicted at each level of the hierarchy to form a new graph at the next level. Unlike fully unsupervised hierarchical clustering, the choice of grouping and complexity criteria stems naturally from supervision in the training set.

Source	Audiovisual SlowFast Networks for Video Recognition
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com