Unverified50% confidenceFactExact time
Guo et al. in 2017 found that deep models on ImageNet had average confidence far higher than actual accuracy, and temperature scaling is a simple and effective post-processing calibration correction method.
1
Sources
50%
Confidence
Long-term
Relevance
10/14/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedExperiments on real-world datasets show that integrating deep sequence models into the conformal prediction framework improves the quality of time series prediction intervals, balancing both coverage rate and interval width.67% similarUnverified深度重绘使用MiDaS、DPT等单目深度估计模型从原图中自动推断深度图67% similarUnverifiedThe combination of signal decomposition techniques (such as EMD) with deep learning (such as LSTM) has been proven to be an effective approach for improving thermal load prediction accuracy.65% similarUnverifiedThe research team used the same dataset employed during the development of the DeepTMHMM model and evaluated model performance using 5-fold cross-validation.65% similarUnverifiedIn an era where general capabilities are approaching saturation, in-depth evaluations in vertical domains are becoming a key benchmark for measuring the true value of AI models.64% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1029237API
curl https://kongchang.com/api/v1/knowledge/claims/1029237MCP
get_claim(id=1029237)