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Found 12 papers, 6 papers with code
Toward Open-ended Embodied Tasks Solving
Empowering embodied agents, such as robots, with Artificial Intelligence (AI) has become increasingly important in recent years.
Secure and Fast Asynchronous Vertical Federated Learning via Cascaded Hybrid Optimization
Meanwhile, the upstream model (server) is updated with first-order optimization (FOO) locally, which significantly improves the convergence rate, making it feasible to train the large models without compromising privacy and security.
Class Overwhelms: Mutual Conditional Blended-Target Domain Adaptation
We demonstrate that domain labels are not directly necessary for BTDA if categorical distributions of various domains are sufficiently aligned even facing the imbalance of domains and the label distribution shift of classes.
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Multi-target Domain Adaptation
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Blended-target Domain Adaptation
Label shift of blended-target domain adaptation
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When Source-Free Domain Adaptation Meets Learning with Noisy Labels
We also prove that such a difference makes existing LLN methods that rely on their distribution assumptions unable to address the label noise in SFDA.
Foresee What You Will Learn: Data Augmentation for Domain Generalization in Non-stationary Environment
In this paper, we formulate such problems as Evolving Domain Generalization, where a model aims to generalize well on a target domain by discovering and leveraging the evolving pattern of the environment.
Dynamically Instance-Guided Adaptation: A Backward-Free Approach for Test-Time Domain Adaptive Semantic Segmentation
In this paper, we study the application of Test-time domain adaptation in semantic segmentation (TTDA-Seg) where both efficiency and effectiveness are crucial.
On Learning Fairness and Accuracy on Multiple Subgroups
In the upper-level, the fair predictor is updated to be close to all subgroup specific predictors.
Evolving Domain Generalization
Domain generalization aims to learn a predictive model from multiple different but related source tasks that can generalize well to a target task without the need of accessing any target data.
Directional Domain Generalization
Domain generalization aims to learn a predictive model from multiple different but related source tasks that can generalize well to a target task without the need of accessing any target data.
Aggregating From Multiple Target-Shifted Sources
Multi-source domain adaptation aims at leveraging the knowledge from multiple tasks for predicting a related target domain.
Unified Principles For Multi-Source Transfer Learning Under Label Shifts
We study the label shift problem in multi-source transfer learning and derive new generic principles to control the target generalization risk.
Pelee: A Real-Time Object Detection System on Mobile Devices
In this study, we propose an efficient architecture named PeleeNet, which is built with conventional convolution instead.
