These models such as Segment Anything Model (SAM) are generalized models trained using huge datasets. Currently, ongoing research focuses on exploring the effective utilization of these generalized models for Specific domains, such as medical imaging. However, in medical imaging, the lack of training...
In this article, we review the recent progress in the development of fullerene-free OSCs based on PDI derivatives, with a focus on the molecular structure fine-tuning approach.PDI derivatives are a class of conjugated polymers that possess a highly electron-rich core, which confers excellent charg...
As the headline indicates, while the field starts to converge into the term “continued pre-training” a definite term for the fine-tuning approach discussed in this sections has yet to be agreed on by community. But what is this fine-tuning approach really abou...
The most intuitive partial fine-tuning approach is to update only the outer layers of the neural network. In most model architectures, the inner layers of the model (closest to the input layer) capture only broad, generic features: for example, in a CNN used for image classification, early ...
We present QLoRA, an efficientfinetuningapproach that reduces memory usage enough to finetune a 65B parameter model on a single 48GB GPU while preserving full 16-bit finetuning task performance. QLoRA backpropagates gradients through a frozen,4-bit quantizedpretrained language model into Low Rank...
The failure of this approach stems from two key factors: (i). The pre-training data of CLIP and the training data for the downstream task of plant disease detection have a significant domain gap. Plant disease recognition involves a more fine-grained classification task as leaf samples can be...
题目:Fine-Tuning Pre-Trained Language Model withWeak Supervision: A Contrastive-Regularized Self-Training Approach 来源:arxiv 原文链接:Fine-Tuning Pre-trained Language Model with Weak Supervision…
LoftQ builds on the principles ofLoRA(opens in new tab)andQLoRA(opens in new tab). LoRA is a method that greatly reduces the number of parameters needed for training, decreasing the memory requirements for fine-tuning. QLoRA is a fine-tuning approach that uses 4-bit quantized, froze...
Table 4: Testing AD-Drop on a larger model. MethodsMRPCRTE RoBERTalarge90.83±0.7585.99±0.86 +AD-Drop91.62±0.5388.01±0.48 Table 5: Testing AD-Drop in few-shot settings. RoBERTa with AD-Drop achieves higherperformanceand lower deviations than that with the original fine-tuning approach. ...
A Fine-Tuning Based Approach for Daily Activity Recognition between Smart HomesSMART homesHUMAN activity recognitionACTIVITIES of daily livingRECOGNITION (Psychology)KNOWLEDGE transferDATA distributionDaily activity recognition between different smart home environments faces some challenges, such as an insufficient...