FINE Samples for Learning with Noisy Labels. 来自 arXiv.org 喜欢 0 阅读量: 104 作者:T Kim,J Ko,S Cho,J Choi,SY Yun 摘要: Modern deep neural networks (DNNs) become frail when the datasets contain noisy (incorrect) class labels. Robust techniques in the presence of noisy labels can be...
Since it is practically impossible to categorize all noisy samples correctly, we further process them in a fine-grained manner via modeling the credibility of each sample. Specifically, for the clean set, we deliberately design a memory-based modulation scheme to dynamically adjust the contribution ...
Recently, there has been growing interest in developing robust models for fine-grained classification with noisy labels. One approach is to use deep neural networks (DNNs) that are specifically designed to handle noisy labels. DNNs have shown impressive performance on various machine learning tasks, ...
Class-Balanced Loss Based on Effective Number of Samples. CVPR 2019 computer-visiondeep-learningtensorflowimagenetcvprfine-grainedinaturalistfine-grained-classificationfine-grained-visual-categorizationcvpr2019cloud-tpu UpdatedAug 29, 2021 Python Multi-label Classification with BERT; Fine Grained Sentiment Analy...
Robust fine-grained image classification with noisy labelsThe 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......
A sample with a score greater than the threshold [Math Processing Error]γ will be classified as unknown classes, and vice versa. Datasets For our experiments on open-set recognition, we employ the Cotton32, Mango33, Strawberry34, and Tomato35 disease datasets. We illustrate samples from these...
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nan 相关学科: Fine-Grained Visual CategorizationPlankton ClassificationPower NormalizationImage RankingDeep AttentionVehicle Re-IdentificationFine-Grained Image ClassificationSpatial TransformerFace VerificationObject Discovery 学科讨论 暂无讨论内容,你可以发起讨论推荐文献 发布年度 会议/ 期刊 按被引用数学科...
Informally, claims are the unit for the checking. Previous works use sentences in the response as claims (SelfCheckGPT), or generate short phrases (i.e. sub-sentences) as the claims produced by LLM’s in-context learning (FActScore,FACTOOL). This work explores the approach of representing ...
For instance, if two samples in the training set have very similar visual content but different class labels, minimizing the cross-entropy loss will force the neural network to learn features that distinguish these two images with high confidence—potentially forcing the network to learn sampl...