Unsupervised Domain Adaptation through Iterative Consensus Shift in a Multi-Task Graph

by   Emanuela Haller, et al.

Babies learn with very little supervision by observing the surrounding world. They synchronize the feedback from all their senses and learn to maintain consistency and stability among their internal states. Such observations inspired recent works in multi-task and multi-modal learning, but existing methods rely on expensive manual supervision. In contrast, our proposed multi-task graph, with consensus shift learning, relies only on pseudo-labels provided by expert models. In our graph, every node represents a task, and every edge learns to transform one input node into another. Once initialized, the graph learns by itself on virtually any novel target domain. An adaptive selection mechanism finds consensus among multiple paths reaching a given node and establishes the pseudo-ground truth at that node. Such pseudo-labels, given by ensemble pathways in the graph, are used during the next learning iteration when single edges distill this distributed knowledge. We validate our key contributions experimentally and demonstrate strong performance on the Replica dataset, superior to the very few published methods on multi-task learning with minimal supervision.


page 4

page 8


Semi-Supervised Learning for Multi-Task Scene Understanding by Neural Graph Consensus

We address the challenging problem of semi-supervised learning in the co...

Unsupervised Domain Adaptation: A Multi-task Learning-based Method

This paper presents a novel multi-task learning-based method for unsuper...

Generative Pseudo-label Refinement for Unsupervised Domain Adaptation

We investigate and characterize the inherent resilience of conditional G...

Multi-Task Hypergraphs for Semi-supervised Learning using Earth Observations

There are many ways of interpreting the world and they are highly interd...

RefRec: Pseudo-labels Refinement via Shape Reconstruction for Unsupervised 3D Domain Adaptation

Unsupervised Domain Adaptation (UDA) for point cloud classification is a...

Self-supervised Hypergraphs for Learning Multiple World Interpretations

We present a method for learning multiple scene representations given a ...

Curriculum Graph Co-Teaching for Multi-Target Domain Adaptation

In this paper we address multi-target domain adaptation (MTDA), where gi...

Please sign up or login with your details

Forgot password? Click here to reset