Multi-target Domain Adaptation
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Benchmarks
Most implemented
Unsupervised Domain Adaptation by Backpropagation
Minimum Class Confusion for Versatile Domain Adaptation
Merge-Friendly Post-Training Quantization for Multi-Target Domain Adaptation
COSMo: CLIP Talks on Open-Set Multi-Target Domain Adaptation
ConvLoRA and AdaBN based Domain Adaptation via Self-Training
Papers
Merge-Friendly Post-Training Quantization for Multi-Target Domain Adaptation
Model merging has emerged as a powerful technique for combining task-specific weights, achieving superior performance in multi-target domain adaptation. However, when applied to practical scenarios, such as quantized mod…
Domain AdaptationMulti-target Domain AdaptationQuantizationGrInAdapt: Scaling Retinal Vessel Structural Map Segmentation Through Grounding, Integrating and Adapting Multi-device, Multi-site, and Multi-modal Fundus Domains
Retinal vessel segmentation is critical for diagnosing ocular conditions, yet current deep learning methods are limited by modality-specific challenges and significant distribution shifts across imaging devices, resoluti…
Decision MakingDomain AdaptationMulti-target Domain AdaptationRetinal Vessel Segmentation+1COSMo: CLIP Talks on Open-Set Multi-Target Domain Adaptation
Multi-Target Domain Adaptation (MTDA) entails learning domain-invariant information from a single source domain and applying it to multiple unlabeled target domains. Yet, existing MTDA methods predominantly focus on addr…
Domain AdaptationMulti-target Domain AdaptationOpen-Set Multi-Target Domain AdaptationPrompt LearningTraining-Free Model Merging for Multi-target Domain Adaptation
In this paper, we study multi-target domain adaptation of scene understanding models. While previous methods achieved commendable results through inter-domain consistency losses, they often assumed unrealistic simultaneo…
Domain AdaptationMulti-target Domain AdaptationScene UnderstandingOurDB: Ouroboric Domain Bridging for Multi-Target Domain Adaptive Semantic Segmentation
Multi-target domain adaptation (MTDA) for semantic segmentation poses a significant challenge, as it involves multiple target domains with varying distributions. The goal of MTDA is to minimize the domain discrepancies a…
Domain AdaptationMulti-target Domain AdaptationSemantic SegmentationConvLoRA and AdaBN based Domain Adaptation via Self-Training
Existing domain adaptation (DA) methods often involve pre-training on the source domain and fine-tuning on the target domain. For multi-target domain adaptation, having a dedicated/separate fine-tuned network for each ta…
Domain AdaptationMulti-target Domain Adaptation