**Fishr** is a learning scheme to enforce domain invariance in the space of the gradients of the loss function: specifically, it introduces a regularization term that matches the domain-level variances of gradients across training domains. Critically, the strategy exhibits close relations with the Fisher Information and the Hessian of the loss. Forcing domain-level gradient covariances to be similar during the learning procedure eventually aligns the domain-level loss landscapes locally around the final weights.

**Asynchronous Interaction Aggregation**, or **AIA**, is a network that leverages different interactions to boost action detection. There are two key designs in it: one is the Interaction Aggregation structure (IA) adopting a uniform paradigm to model and integrate multiple types of interaction; the other is the Asynchronous Memory Update algorithm (AMU) that enables us to achieve better performance by modeling very long-term interaction dynamically.

Asynchronous Interaction Aggregation

Asynchronous Interaction Aggregation for Action Detection

Fishr

Fishr: Invariant Gradient Variances for Out-of-Distribution Generalization

In machine learning, a hyperparameter is a parameter whose value is used to control learning process, and HPO is the problem of choosing a set of optimal hyperparameters for a learning algorithm.

Source	Fishr: Invariant Gradient Variances for Out-of-Distribution Generalization
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com