Fine-Tuning is Fine, if Calibrated
… calibration as a promising solution because we find that the only major damage in the fine-tuned
model is the biased logits towards fine-tuning classes. In other words, if the fine-tuned …
model is the biased logits towards fine-tuning classes. In other words, if the fine-tuned …
Calibrated language model fine-tuning for in-and out-of-distribution data
… issue, we propose a regularized fine-tuning method. Our method introduces two types of
regularization for better calibration: (1) On-… On the other hand, if we use NBAUCC, we will have …
regularization for better calibration: (1) On-… On the other hand, if we use NBAUCC, we will have …
DR-Tune: Improving Fine-tuning of Pretrained Visual Models by Distribution Regularization with Semantic Calibration
… maps, largely facilitating optimizing the downstream encoder towards the downstream task;
2) SC greatly reduces the semantic drift and the classification bias is thus alleviated when …
2) SC greatly reduces the semantic drift and the classification bias is thus alleviated when …
Cal-ql: Calibrated offline rl pre-training for efficient online fine-tuning
… fine-tuning, when the value function is pre-trained with Cal-QL, and show that enforcing
calibration (… [52], but studies the impact of calibration on fine-tuning. We also remark that we …
calibration (… [52], but studies the impact of calibration on fine-tuning. We also remark that we …
Calibration of pre-trained transformers
… For each task, we consider in-domain as well as challenging … 1) when used out-of-the-box,
pretrained models are calibrated … calibration error (ECE), both averaged across 5 fine-tuning …
pretrained models are calibrated … calibration error (ECE), both averaged across 5 fine-tuning …
Co-tuning for transfer learning
… While state-of-the-art fine-tuning techniques mainly focus on how to impose regularization
when data … We also introduce the calibration option if source validation data or the confidence-…
when data … We also introduce the calibration option if source validation data or the confidence-…
Robust fine-tuning of zero-shot models
… We observe a similar phenomenon when fine-tuning with … When pre-trained models are
adapted to a specific distri… Calibrated ensembles: A simple way to mitigate ID-OOD accuracy …
adapted to a specific distri… Calibrated ensembles: A simple way to mitigate ID-OOD accuracy …
Accurate post training quantization with small calibration sets
… fitting than the standard fine-tuning approaches, and can be used even on a very small
calibration set; and (2) … If the system at hand supports more extensive fine-tuning process and the …
calibration set; and (2) … If the system at hand supports more extensive fine-tuning process and the …
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
… When fine-tuning on ImageNet we also evaluate on … Calibration. While ensembles improve
model calibration (Guo et … As hyperparameters can also have an effect on calibration, we …
model calibration (Guo et … As hyperparameters can also have an effect on calibration, we …
The impact of domain shift on the calibration of fine-tuned models
… informed design decisions when calibration of the fine-tuned model is a major concern. …
and fine-tuning. In the following subsections, we provide some background on calibration and …
and fine-tuning. In the following subsections, we provide some background on calibration and …
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