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Found 8 papers, 3 papers with code
Online Test-Time Adaptation of Spatial-Temporal Traffic Flow Forecasting
To make the model trained on historical data better adapt to future data in a fully online manner, this paper conducts the first study of the online test-time adaptation techniques for spatial-temporal traffic flow forecasting problems.
Ranked #4 on
Traffic Prediction
on PeMS07
A Unified Framework for Unsupervised Domain Adaptation based on Instance Weighting
Specifically, the proposed LIWUDA method constructs a weight network to assign weights to each instance based on its probability of belonging to common classes, and designs Weighted Optimal Transport (WOT) for domain alignment by leveraging instance weights.
Selective Partial Domain Adaptation
To solve this problem, we propose a Selective Partial Domain Adaptation (SPDA) method, which selects useful data for the adaptation to the target domain.
Ranked #1 on
Partial Domain Adaptation
on VisDA2017
Domain Adaptation via Bidirectional Cross-Attention Transformer
Specifically, in BCAT, we design a weight-sharing quadruple-branch transformer with a bidirectional cross-attention mechanism to learn domain-invariant feature representations.
Domain Adaptation by Maximizing Population Correlation with Neural Architecture Search
Base on the PC function, we propose a new method called Domain Adaptation by Maximizing Population Correlation (DAMPC) to learn a domain-invariant feature representation for DA.
Multi-Objective Meta Learning
Empirically, we show the effectiveness of the proposed MOML framework in several meta learning problems, including few-shot learning, neural architecture search, domain adaptation, and multi-task learning.
Multi-Task Adversarial Attack
More specifically, MTA uses a generator for adversarial perturbations which consists of a shared encoder for all tasks and multiple task-specific decoders.
Deep Multi-Task Augmented Feature Learning via Hierarchical Graph Neural Network
The proposed feature augmentation strategy can be used in many deep multi-task learning models.
