Importance Reweighting for Biquality Learning
- URL: http://arxiv.org/abs/2010.09621v5
- Date: Mon, 20 Sep 2021 09:56:02 GMT
- Title: Importance Reweighting for Biquality Learning
- Authors: Pierre Nodet and Vincent Lemaire and Alexis Bondu and Antoine
Cornu\'ejols
- Abstract summary: This paper proposes an original, encompassing, view of Weakly Supervised Learning.
It results in the design of generic approaches capable of dealing with any kind of label noise.
In this paper, we propose a new reweigthing scheme capable of identifying noncorrupted examples in the untrusted dataset.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The field of Weakly Supervised Learning (WSL) has recently seen a surge of
popularity, with numerous papers addressing different types of "supervision
deficiencies", namely: poor quality, non adaptability, and insufficient
quantity of labels. Regarding quality, label noise can be of different types,
including completely-at-random, at-random or even not-at-random. All these
kinds of label noise are addressed separately in the literature, leading to
highly specialized approaches. This paper proposes an original, encompassing,
view of Weakly Supervised Learning, which results in the design of generic
approaches capable of dealing with any kind of label noise. For this purpose,
an alternative setting called "Biquality data" is used. It assumes that a small
trusted dataset of correctly labeled examples is available, in addition to an
untrusted dataset of noisy examples. In this paper, we propose a new
reweigthing scheme capable of identifying noncorrupted examples in the
untrusted dataset. This allows one to learn classifiers using both datasets.
Extensive experiments that simulate several types of label noise and that vary
the quality and quantity of untrusted examples, demonstrate that the proposed
approach outperforms baselines and state-of-the-art approaches.
Related papers
- 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) - Robust Assignment of Labels for Active Learning with Sparse and Noisy
Annotations [0.17188280334580192]
Supervised classification algorithms are used to solve a growing number of real-life problems around the globe.
Unfortunately, acquiring good-quality annotations for many tasks is infeasible or too expensive to be done in practice.
We propose two novel annotation unification algorithms that utilize unlabeled parts of the sample space.
arXiv Detail & Related papers (2023-07-25T19:40:41Z) - Soft Curriculum for Learning Conditional GANs with Noisy-Labeled and
Uncurated Unlabeled Data [70.25049762295193]
We introduce a novel conditional image generation framework that accepts noisy-labeled and uncurated data during training.
We propose soft curriculum learning, which assigns instance-wise weights for adversarial training while assigning new labels for unlabeled data.
Our experiments show that our approach outperforms existing semi-supervised and label-noise robust methods in terms of both quantitative and qualitative performance.
arXiv Detail & Related papers (2023-07-17T08:31:59Z) - Rethinking the Value of Labels for Instance-Dependent Label Noise
Learning [43.481591776038144]
noisy labels in real-world applications often depend on both the true label and the features.
In this work, we tackle instance-dependent label noise with a novel deep generative model that avoids explicitly modeling the noise transition matrix.
Our algorithm leverages casual representation learning and simultaneously identifies the high-level content and style latent factors from the data.
arXiv Detail & Related papers (2023-05-10T15:29:07Z) - Trustable Co-label Learning from Multiple Noisy Annotators [68.59187658490804]
Supervised deep learning depends on massive accurately annotated examples.
A typical alternative is learning from multiple noisy annotators.
This paper proposes a data-efficient approach, called emphTrustable Co-label Learning (TCL)
arXiv Detail & Related papers (2022-03-08T16:57:00Z) - Tackling Instance-Dependent Label Noise via a Universal Probabilistic
Model [80.91927573604438]
This paper proposes a simple yet universal probabilistic model, which explicitly relates noisy labels to their instances.
Experiments on datasets with both synthetic and real-world label noise verify that the proposed method yields significant improvements on robustness.
arXiv Detail & Related papers (2021-01-14T05:43:51Z) - A Second-Order Approach to Learning with Instance-Dependent Label Noise [58.555527517928596]
The presence of label noise often misleads the training of deep neural networks.
We show that the errors in human-annotated labels are more likely to be dependent on the difficulty levels of tasks.
arXiv Detail & Related papers (2020-12-22T06:36:58Z) - EvidentialMix: Learning with Combined Open-set and Closed-set Noisy
Labels [30.268962418683955]
We study a new variant of the noisy label problem that combines the open-set and closed-set noisy labels.
Our results show that our method produces superior classification results and better feature representations than previous state-of-the-art methods.
arXiv Detail & Related papers (2020-11-11T11:15:32Z) - Label Noise Types and Their Effects on Deep Learning [0.0]
In this work, we provide a detailed analysis of the effects of different kinds of label noise on learning.
We propose a generic framework to generate feature-dependent label noise, which we show to be the most challenging case for learning.
For the ease of other researchers to test their algorithms with noisy labels, we share corrupted labels for the most commonly used benchmark datasets.
arXiv Detail & Related papers (2020-03-23T18:03:39Z) - Confidence Scores Make Instance-dependent Label-noise Learning Possible [129.84497190791103]
In learning with noisy labels, for every instance, its label can randomly walk to other classes following a transition distribution which is named a noise model.
We introduce confidence-scored instance-dependent noise (CSIDN), where each instance-label pair is equipped with a confidence score.
We find with the help of confidence scores, the transition distribution of each instance can be approximately estimated.
arXiv Detail & Related papers (2020-01-11T16:15:41Z)
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.