Unverified50% confidenceFactExact time
ViTs usually require larger-scale data for pretraining, otherwise they tend to underfit on small datasets.
1
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50%
Confidence
Long-term
Relevance
10/14/2026
First Seen
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Related Claims
UnverifiedCompared to full pre-training, SFT requires far less data and compute, but still requires a GPU cluster, a distributed training framework, and checkpoint management and experiment tracking capabilities.76% similarUnverifiedOn small datasets, DETR weights pre-trained on large-scale datasets such as COCO should be used for fine-tuning rather than training from scratch74% similarUnverifiedGlobal classification is not suitable for local tampering detection, and in small-data scenarios, self-supervised pre-training and domain priors are often more effective than larger models.73% similarUnverifiedThe classifier was trained using only supervised fine-tuning (SFT), public datasets, and two simple tricks, without relying on closed-source data or expensive custom pipelines70% similarUnverifiedFrontier general-purpose large models require no task-specific optimization but are often only accessible via API, whereas smaller specialized models require targeted training but can be deployed locally, making them more suitable for medical data privacy scenarios.69% similar
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