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Found 5 papers, 1 papers with code
VLLaVO: Mitigating Visual Gap through LLMs
Recent advances achieved by deep learning models rely on the independent and identically distributed assumption, hindering their applications in real-world scenarios with domain shifts.
Domain-Guided Conditional Diffusion Model for Unsupervised Domain Adaptation
However, the performance of existing UDA methods is constrained by the large domain shift and limited target domain data.
Diffusion-based Target Sampler for Unsupervised Domain Adaptation
The generated samples can well simulate the data distribution of the target domain and help existing UDA methods transfer from the source domain to the target domain more easily, thus improving the transfer performance.
Blindfolded Attackers Still Threatening: Strict Black-Box Adversarial Attacks on Graphs
To bridge the gap between theoretical graph attacks and real-world scenarios, in this work, we propose a novel and more realistic setting: strict black-box graph attack, in which the attacker has no knowledge about the victim model at all and is not allowed to send any queries.
Unsupervised Adversarially-Robust Representation Learning on Graphs
Additionally, we explore a provable connection between the robustness of the unsupervised graph encoder and that of models on downstream tasks.
