Unverified50% confidenceBenchmarkExact time
In the ImageNet-1k supervised training setting, reViT-B/16 trained from scratch reaches accuracy comparable to DeiT III while reducing stored parameters by about 70%.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/13/2026
First Seen
Valid until: 1/11/2027
Sources
Related Entities
Related Claims
UnverifiedResearch such as Microsoft's BitNet shows that if 1-bit quantization-aware training (QAT) is applied from the pre-training stage, accuracy loss can be controlled, whereas post-training quantization (PTQ) typically incurs greater loss.75% similarUnverified1-bit/1.58-bit要求从训练阶段就以低精度权重为目标,与训练后量化方案本质不同74% similarUnverifiedFor image data, the commonly used ImageNet pre-training mean [0.485, 0.456, 0.406] and standard deviation [0.229, 0.224, 0.225] have become the de facto standard.72% similarUnverifiedMethods such as freezing LLM backbone parameters during the visual alignment training phase, staged training, data ratio replay, and LoRA can mitigate catastrophic forgetting in multimodal training.72% similarUnverified一位Reddit用户报告使用约300万参数的预训练U-Net,训练集550张图像、验证集150张图像,单独用交叉熵损失时收敛良好,引入Dice损失后Dice分数出现剧烈波动70% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1013265API
curl https://kongchang.com/api/v1/knowledge/claims/1013265MCP
get_claim(id=1013265)