# AdaGraph: Unifying Predictive and Continuous Domain Adaptation through Graphs

## TL;DR

AdaGraph introduces a novel deep architecture that unifies predictive and continuous domain adaptation by leveraging graph-structured auxiliary domain information, enabling better generalization across visual domains without target data during training.

## Contribution

This work is the first to propose a deep architecture for predictive domain adaptation that uses graphs to incorporate auxiliary domain information and supports continuous adaptation at test time.

## Key findings

- Effective on three benchmark datasets
- Outperforms existing domain adaptation methods
- Enables continuous adaptation during testing

## Abstract

The ability to categorize is a cornerstone of visual intelligence, and a key functionality for artificial, autonomous visual machines. This problem will never be solved without algorithms able to adapt and generalize across visual domains. Within the context of domain adaptation and generalization, this paper focuses on the predictive domain adaptation scenario, namely the case where no target data are available and the system has to learn to generalize from annotated source images plus unlabeled samples with associated metadata from auxiliary domains. Our contributionis the first deep architecture that tackles predictive domainadaptation, able to leverage over the information broughtby the auxiliary domains through a graph. Moreover, we present a simple yet effective strategy that allows us to take advantage of the incoming target data at test time, in a continuous domain adaptation scenario. Experiments on three benchmark databases support the value of our approach.