Besides, the two-stages fine-tuning approach increases the HuSHeM and SCIAN-Morpho performance up to 92.1% and 73.2% without any manual intervention in contrast to competitors employing manual cropping, rotation and biased augmentation steps.
Inspired by the supervised fine-tuning in chatbot domains, we prioritize a two-stage fine-tuning process: first conducting supervised fine-tuning to orient the LLM towards time-series data, followed by task-specific downstream finetuning. Furthermore, to unlock the flexibility of pre-trained LLMs...
In contrast, the two-stage approach first trains the experts independently, enabling them to specialize in their respective tasks (nuclei segmentation, normal edge segmentation, and clustered edge segmentation). In the second stage, their collaboration with the gating network is fine-tuned. Across ...
A two-stage approach is used to build the ML models, GTS-ML from the gun to the sample and STD-ML from the sample to the detector. The BD patterns as the input and the electron beam properties as the data label are applied to train the STD-ML model. To automate the UED instrument...
The framework includes two learning stages: pre-training and fine-tuning. In the pre-training stage, the potential feature representation is learned from the unlabeled crack image. Crack images and pavement background images are used in the training data so that the model learns the distinguishable...
which can thus be taken as indicating a significant advancement in this area. It is the use of a hybrid classifier and the fine-tuning of weightings using the CME-TWO algorithm that leads to the marked improvement in accuracy and reliability. Moreover, the team says that there is still roo...
finetuning-tasks: image-object-detection finetune-min-sku-spec: 4|1|28|176 finetune-recommended-sku: Standard_NC4as_T4_v3, Standard_NC6s_v3, Standard_NC8as_T4_v3, Standard_NC12s_v3, Standard_NC16as_T4_v3, Standard_NC24s_v3, Standard_NC64as_T4_v3, Standard_NC96ads_A100_v4, Stand...
To optimize the feature extraction module and enhance the final model's performance by supplying initial parameters, we pre-train the network using in the protein-peptide dataset and transfer the network parameters to the PPI site task for fine-tuning in the second stage of transfer learning. (...
In addition, two strategies, adaptive proposal jitter and proposal fine-tuning, are proposed for accurate object localization. Through these two strategies, the networks are encouraged to Declaration of Competing Interest The authors declare that they have no known competing financial interests or ...
In this work, we propose a cascade CNN architecture with two stages: the first stage manages to produce an initial disparity image with fine details, while the second stage explicitly refines/rectifies the initial dis- parity with residual signals across multiple scales. We call our ...