Unverified50% confidenceFactExact time
20 samples are insufficient to support a reliable multi-class model, and forcing subdivision will introduce noise and overfitting.
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50%
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Long-term
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9/11/2026
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UnverifiedIn few-shot scenarios (e.g., approximately 20 samples per class), multi-class subdivision should be abandoned, all defects should be collapsed into a binary classification problem (defective/non-defective), and specific classification should be left to manual post-processing.74% similarVerifiedIn scenarios with extreme class imbalance, even an accuracy as high as 95% may indicate that the model is completely unable to identify the minority class.70% similarUnverified非自回归语音模型通常比自回归模型快10-100倍，但表现力受限于预测器准确性67% similarUnverifiedTop academic deepfake detectors can see their accuracy plummet from 99% to below 60% when faced with previously unseen generative models, a phenomenon known as the cross-model generalization problem.66% similarUnverifiedThe 2023 Microsoft Research paper 'LIMA: Less Is More for Alignment' showed that using only 1,000 carefully curated high-quality samples can enable a base model to exhibit conversational capabilities comparable to fine-tuning on tens of thousands of samples.66% similar
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