noisy labels, ignoring the fact that the difficulties of noisy samples are different, thus a rigid and unified data selection pipeline cannot tackle this problem well. In this paper, we first propose a coarse-to
Noisy labelsDeep learningRobust lossThe Visual Computer - Since annotating fine-grained labels requires special expertise, label annotations often lack quality for many real-world fine-grained image classifications (FGIC). Due to the...doi:10.1007/s00371-022-02686-wTan, Xinxing...
Some works [14,15] use disagreement to refine the optimization step for noisy labels. For example, co-teaching [15] iteratively trains two models. Show abstract On better detecting and leveraging noisy samples for learning with severe label noise 2023, Pattern Recognition Show abstract Hierarchical...
By introducing the coarse-to- fine two-stage strategy, the client can adaptively eliminate the noisy data. Meanwhile, we proposed a balanced progressive learning framework, It leverages the self-paced learning to sort the training samples from simple to difficult, which can evenly construct the ...
LLMs can be fine-tuned for most industries or domains; the key requirement is high-quality training data with accurate labeling. Through my experience developing LLMs andmachine learning(ML) tools for universities and clients across industries like finance and insurance, I’ve gathered several prove...
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Meanwhile, the forgetting loss function is used for samples belonging to Df, as (5). (4)Lossretaining=0.4×lossSTarget+0.6×lossSCT (5)Lossforgetting=lossSITwhere n is a data sample; ϵ is a very small value added to ensure no probability is zero. The training process of the ...
Liang. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging, 35(5):1299–1312, 2016.作者提出的 AIFT 首次将 actice learning 整合到 fine-tuning CNNs 网络结构中,通过 continuous fashion { 不知道怎么翻译 } 的方式使得 CNNs 在...
To this end, we present a hierarchical fine-grained formulation for IFDL representation learning. Specifically, we first represent forgery attributes of a manipulated image with multiple labels at different levels. Then, we perform fine-grained classification at these levels using the hierarchical ...
(2021). Understanding and improv- ing early stopping for learning with noisy labels. Advances in Neural Information Processing Systems, 34, 24392–24403. Chen, Y., Bai, Y., Zhang, W., & Mei, T. (2019). Destruction and construction learning for fine-grained image recognition. In CVPR (...