Prefer-DAS integrates self-training, prompt-guided contrastive learning, local direct preference optimization (LPO), and unsupervised preference optimization (UPO) to achieve effective domain adaptive segmentation in electron microscopy using sparse prompts and local preferences.
Domain-adversarial training of neural networks
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UNVERDICTED 3representative citing papers
Offline adversarial domain generalization plus online dual-memory replay enables test-time adaptation for motor fault diagnosis under unseen loads and speeds.
Threshold Modulation dynamically adjusts firing thresholds in SNNs via neuronal dynamics-inspired normalization to enable online test-time adaptation under distribution shifts.
citing papers explorer
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Prefer-DAS: Learning from Local Preferences and Sparse Prompts for Domain Adaptive Segmentation of Electron Microscopy
Prefer-DAS integrates self-training, prompt-guided contrastive learning, local direct preference optimization (LPO), and unsupervised preference optimization (UPO) to achieve effective domain adaptive segmentation in electron microscopy using sparse prompts and local preferences.
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Replay-guided Test-time Adaptation for Fault Diagnosis Under Unseen Operating Conditions
Offline adversarial domain generalization plus online dual-memory replay enables test-time adaptation for motor fault diagnosis under unseen loads and speeds.
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Threshold Modulation for Online Test-Time Adaptation of Spiking Neural Networks
Threshold Modulation dynamically adjusts firing thresholds in SNNs via neuronal dynamics-inspired normalization to enable online test-time adaptation under distribution shifts.