A large-scale benchmark of 17 WHAR models across 30 datasets finds predictive performance has plateaued while efficiency favors compact neural models and random forests on the Pareto frontier.
author Xiang, S
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
RABC-Net achieves 86.58% DICE and 79.47% JAC on skin lesion segmentation across ISIC-2017, ISIC-2018, and PH2 using only pseudo-labels and no manual masks for training or adaptation.
μMatch applies student-teacher semi-supervised methods with foundation models to improve segmentation of mitochondria, nuclei, and neurites in EM images over strong baselines.
Fine-tuning pre-trained embeddings is necessary for best performance in predicting AAV vector viability, with sequence-level representations excelling post-fine-tuning in datasets with sparse localized mutations.
citing papers explorer
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WHAR Arena: Benchmarking the State of the Art in Efficient Wearable Human Activity Recognition
A large-scale benchmark of 17 WHAR models across 30 datasets finds predictive performance has plateaued while efficiency favors compact neural models and random forests on the Pareto frontier.
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RABC-Net: Reliability-Aware Annotation-Free Skin Lesion Segmentation for Low-Resource Dermoscopy
RABC-Net achieves 86.58% DICE and 79.47% JAC on skin lesion segmentation across ISIC-2017, ISIC-2018, and PH2 using only pseudo-labels and no manual masks for training or adaptation.
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$\mu$Match: Foundation Models for Semi-supervised Learning and Domain Adaptation in EM
μMatch applies student-teacher semi-supervised methods with foundation models to improve segmentation of mitochondria, nuclei, and neurites in EM images over strong baselines.
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Exploring the limits of pre-trained embeddings in machine-guided protein design: a case study on predicting AAV vector viability
Fine-tuning pre-trained embeddings is necessary for best performance in predicting AAV vector viability, with sequence-level representations excelling post-fine-tuning in datasets with sparse localized mutations.