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Found 7 papers, 2 papers with code
FLASH: Federated Learning Across Simultaneous Heterogeneities
The key premise of federated learning (FL) is to train ML models across a diverse set of data-owners (clients), without exchanging local data.
Plug-and-Play Transformer Modules for Test-Time Adaptation
Parameter-efficient tuning (PET) methods such as LoRA, Adapter, and Visual Prompt Tuning (VPT) have found success in enabling adaptation to new domains by tuning small modules within a transformer model.
MeTA: Multi-source Test Time Adaptation
Test time adaptation is the process of adapting, in an unsupervised manner, a pre-trained source model to each incoming batch of the test data (i. e., without requiring a substantial portion of the test data to be available, as in traditional domain adaptation) and without access to the source data.
Effective Restoration of Source Knowledge in Continual Test Time Adaptation
By restoring the knowledge from the source, it effectively corrects the negative consequences arising from the gradual deterioration of model parameters caused by ongoing shifts in the domain.
SUMMIT: Source-Free Adaptation of Uni-Modal Models to Multi-Modal Targets
In this work, we relax both of these assumptions by addressing the problem of adapting a set of models trained independently on uni-modal data to a target domain consisting of unlabeled multi-modal data, without having access to the original source dataset.
Cross-Modal Knowledge Transfer Without Task-Relevant Source Data
In such cases, transferring knowledge from a neural network trained on a well-labeled large dataset in the source modality (RGB) to a neural network that works on a target modality (depth, infrared, etc.)
Unsupervised Multi-source Domain Adaptation Without Access to Source Data
A recent line of work addressed this problem and proposed an algorithm that transfers knowledge to the unlabeled target domain from a single source model without requiring access to the source data.
