这篇论文的核心思想是NL(Negative Learning)也就是利用complementary label(互补标签)对模型进行训练从而提升噪声鲁棒性 0x02 Negative Learning 对于一个图像分类任务,传统的学习策略PL(Positive Learning)利用图像正确的标签:"input image belongs to this label"。但在噪声情况下,PL会提供错误的信息,随着训练的进行会...
Learning deep neural network (DNN) classifier with noisy labels is a challenging task because the DNN can easily over-fit on these noisy labels due to its high capability. In this paper, we present a very simple but effective training paradigm called P-DIFF+, which can train DNN classifiers...
For Vanilla Loss and PLS, direct training works better when learning with symmetric noisy labels under noise rate 0.2. Run the code bellow to reproduce our results: CUDA_VISIBLE_DEVICES=0 python3 main_GLS_direct_train.py --noise_type symmetric --noise_rate 0.2 Warm-up with CE loss When ...
Noisy few-shot learning (NFSL) presents novel challenges primarily due to the interplay between noisy labels and limited training data. While data cleansin... X Que,Q Yu - European Conference on Computer Vision 被引量: 0发表: 2025年 Robust meta gradient learning for high-dimensional data with...
Semi-supervised learning by mixed label propagation In particular, the new framework determines the assignment of class labels by (1) minimizing the energy function associated with positive similarity, and (2) maximizing the energy function associated with negative similarity. Our empirical ... W Tong...
Transfer learning (TL) utilizes data or knowledge from one or more source domains to facilitate learning in a target domain. It is particularly useful when the target domain has very few or no labeled data, due to annotation expense, privacy concerns, et
CSNMF applies correntropy to replace SED as the similarity measure in semi-supervised NMF for enhancing the robustness. Correntropy is a nonlinear and local similarity measure in information theoretic learning [31], and is very robust for handling the non-Gaussian noise and outliers in real world ...
it becomes easier to extract information from the text and apply machine learning algorithms to it. If we skip this step then there is a higher chance that you are working with noisy and inconsistent data. The objective of this step is to clean noise those are less relevant to find the se...
Learning accurate models from ‘omics data is bringing many challenges due to their inherent high-dimensionality, e.g. the number of gene expression variables, and comparatively lower sample sizes, which leads to ill-posed inverse problems. Furthermore, the presence of outliers, either experimental ...
Besides, there might be noisy labels in practical datasets, which impairs the training process of neural networks. In this paper, we propose a semi-supervised algorithm for training data samples with noisy labels by performing selected positive learning and negative learning. To verify the ...