Centrality and Consistency: Two-Stage Clean Samples Identification for
Learning with Instance-Dependent Noisy Labels
- URL: http://arxiv.org/abs/2207.14476v1
- Date: Fri, 29 Jul 2022 04:54:57 GMT
- Title: Centrality and Consistency: Two-Stage Clean Samples Identification for
Learning with Instance-Dependent Noisy Labels
- Authors: Ganlong Zhao, Guanbin Li, Yipeng Qin, Feng Liu, Yizhou Yu
- Abstract summary: We propose a two-stage clean samples identification method.
First, we employ a class-level feature clustering procedure for the early identification of clean samples.
Second, for the remaining clean samples that are close to the ground truth class boundary, we propose a novel consistency-based classification method.
- Score: 87.48541631675889
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Deep models trained with noisy labels are prone to over-fitting and struggle
in generalization. Most existing solutions are based on an ideal assumption
that the label noise is class-conditional, i.e., instances of the same class
share the same noise model, and are independent of features. While in practice,
the real-world noise patterns are usually more fine-grained as
instance-dependent ones, which poses a big challenge, especially in the
presence of inter-class imbalance. In this paper, we propose a two-stage clean
samples identification method to address the aforementioned challenge. First,
we employ a class-level feature clustering procedure for the early
identification of clean samples that are near the class-wise prediction
centers. Notably, we address the class imbalance problem by aggregating rare
classes according to their prediction entropy. Second, for the remaining clean
samples that are close to the ground truth class boundary (usually mixed with
the samples with instance-dependent noises), we propose a novel
consistency-based classification method that identifies them using the
consistency of two classifier heads: the higher the consistency, the larger the
probability that a sample is clean. Extensive experiments on several
challenging benchmarks demonstrate the superior performance of our method
against the state-of-the-art.
Related papers
- Foster Adaptivity and Balance in Learning with Noisy Labels [26.309508654960354]
We propose a novel approach named textbfSED to deal with label noise in a textbfSelf-adaptivtextbfE and class-balancetextbfD manner.
A mean-teacher model is then employed to correct labels of noisy samples.
We additionally propose a self-adaptive and class-balanced sample re-weighting mechanism to assign different weights to detected noisy samples.
arXiv Detail & Related papers (2024-07-03T03:10:24Z) - Extracting Clean and Balanced Subset for Noisy Long-tailed Classification [66.47809135771698]
We develop a novel pseudo labeling method using class prototypes from the perspective of distribution matching.
By setting a manually-specific probability measure, we can reduce the side-effects of noisy and long-tailed data simultaneously.
Our method can extract this class-balanced subset with clean labels, which brings effective performance gains for long-tailed classification with label noise.
arXiv Detail & Related papers (2024-04-10T07:34:37Z) - Shrinking Class Space for Enhanced Certainty in Semi-Supervised Learning [59.44422468242455]
We propose a novel method dubbed ShrinkMatch to learn uncertain samples.
For each uncertain sample, it adaptively seeks a shrunk class space, which merely contains the original top-1 class.
We then impose a consistency regularization between a pair of strongly and weakly augmented samples in the shrunk space to strive for discriminative representations.
arXiv Detail & Related papers (2023-08-13T14:05:24Z) - PASS: Peer-Agreement based Sample Selection for training with Noisy Labels [16.283722126438125]
The prevalence of noisy-label samples poses a significant challenge in deep learning, inducing overfitting effects.
Current methodologies often rely on the small-loss hypothesis or feature-based selection to separate noisy- and clean-label samples.
We propose a new noisy-label detection method, termed Peer-Agreement based Sample Selection (PASS), to address this problem.
arXiv Detail & Related papers (2023-03-20T00:35:33Z) - Combating Noisy Labels in Long-Tailed Image Classification [33.40963778043824]
This paper makes an early effort to tackle the image classification task with both long-tailed distribution and label noise.
Existing noise-robust learning methods cannot work in this scenario as it is challenging to differentiate noisy samples from clean samples of tail classes.
We propose a new learning paradigm based on matching between inferences on weak and strong data augmentations to screen out noisy samples.
arXiv Detail & Related papers (2022-09-01T07:31:03Z) - Learning from Noisy Labels with Coarse-to-Fine Sample Credibility
Modeling [22.62790706276081]
Training deep neural network (DNN) with noisy labels is practically challenging.
Previous efforts tend to handle part or full data in a unified denoising flow.
We propose a coarse-to-fine robust learning method called CREMA to handle noisy data in a divide-and-conquer manner.
arXiv Detail & Related papers (2022-08-23T02:06:38Z) - Label-Noise Learning with Intrinsically Long-Tailed Data [65.41318436799993]
We propose a learning framework for label-noise learning with intrinsically long-tailed data.
Specifically, we propose two-stage bi-dimensional sample selection (TABASCO) to better separate clean samples from noisy samples.
arXiv Detail & Related papers (2022-08-21T07:47:05Z) - Neighborhood Collective Estimation for Noisy Label Identification and
Correction [92.20697827784426]
Learning with noisy labels (LNL) aims at designing strategies to improve model performance and generalization by mitigating the effects of model overfitting to noisy labels.
Recent advances employ the predicted label distributions of individual samples to perform noise verification and noisy label correction, easily giving rise to confirmation bias.
We propose Neighborhood Collective Estimation, in which the predictive reliability of a candidate sample is re-estimated by contrasting it against its feature-space nearest neighbors.
arXiv Detail & Related papers (2022-08-05T14:47:22Z) - Jo-SRC: A Contrastive Approach for Combating Noisy Labels [58.867237220886885]
We propose a noise-robust approach named Jo-SRC (Joint Sample Selection and Model Regularization based on Consistency)
Specifically, we train the network in a contrastive learning manner. Predictions from two different views of each sample are used to estimate its "likelihood" of being clean or out-of-distribution.
arXiv Detail & Related papers (2021-03-24T07:26:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.