Granular-ball Representation Learning for Deep CNN on Learning with Label Noise

• URL: http://arxiv.org/abs/2409.03254v1
• Date: Thu, 5 Sep 2024 05:18:31 GMT
• Title: Granular-ball Representation Learning for Deep CNN on Learning with Label Noise
• Authors: Dawei Dai, Hao Zhu, Shuyin Xia, Guoyin Wang,
• Abstract summary: We propose a general granular-ball computing (GBC) module that can be embedded into a CNN model. In this study, we split the input samples as $gb$ samples at feature-level, each of which can correspond to multiple samples with varying numbers and share one single label. Experiments demonstrate that the proposed method can improve the robustness of CNN models with no additional data or optimization.
• Score: 14.082510085545582
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In actual scenarios, whether manually or automatically annotated, label noise is inevitably generated in the training data, which can affect the effectiveness of deep CNN models. The popular solutions require data cleaning or designing additional optimizations to punish the data with mislabeled data, thereby enhancing the robustness of models. However, these methods come at the cost of weakening or even losing some data during the training process. As we know, content is the inherent attribute of an image that does not change with changes in annotations. In this study, we propose a general granular-ball computing (GBC) module that can be embedded into a CNN model, where the classifier finally predicts the label of granular-ball ($gb$) samples instead of each individual samples. Specifically, considering the classification task: (1) in forward process, we split the input samples as $gb$ samples at feature-level, each of which can correspond to multiple samples with varying numbers and share one single label; (2) during the backpropagation process, we modify the gradient allocation strategy of the GBC module to enable it to propagate normally; and (3) we develop an experience replay policy to ensure the stability of the training process. Experiments demonstrate that the proposed method can improve the robustness of CNN models with no additional data or optimization.

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)
• Combating Label Noise With A General Surrogate Model For Sample Selection [84.61367781175984]
  We propose to leverage the vision-language surrogate model CLIP to filter noisy samples automatically. We validate the effectiveness of our proposed method on both real-world and synthetic noisy datasets.
  arXiv  Detail & Related papers  (2023-10-16T14:43:27Z)
• Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels [61.97359362447732]
  Learning from noisy labels is an important and long-standing problem in machine learning for real applications. In this paper, we reformulate the label-noise problem from a generative-model perspective. Our model achieves new state-of-the-art (SOTA) results on all the standard real-world benchmark datasets.
  arXiv  Detail & Related papers  (2023-05-31T03:01:36Z)
• Self-Evolution Learning for Mixup: Enhance Data Augmentation on Few-Shot Text Classification Tasks [75.42002070547267]
  We propose a self evolution learning (SE) based mixup approach for data augmentation in text classification. We introduce a novel instance specific label smoothing approach, which linearly interpolates the model's output and one hot labels of the original samples to generate new soft for label mixing up.
  arXiv  Detail & Related papers  (2023-05-22T23:43:23Z)
• Knockoffs-SPR: Clean Sample Selection in Learning with Noisy Labels [56.81761908354718]
  We propose a novel theoretically guaranteed clean sample selection framework for learning with noisy labels. Knockoffs-SPR can be regarded as a sample selection module for a standard supervised training pipeline. We further combine it with a semi-supervised algorithm to exploit the support of noisy data as unlabeled data.
  arXiv  Detail & Related papers  (2023-01-02T07:13:28Z)
• Reconstructing Training Data from Model Gradient, Provably [68.21082086264555]
  We reconstruct the training samples from a single gradient query at a randomly chosen parameter value. As a provable attack that reveals sensitive training data, our findings suggest potential severe threats to privacy.
  arXiv  Detail & Related papers  (2022-12-07T15:32:22Z)
• Scalable Penalized Regression for Noise Detection in Learning with Noisy Labels [44.79124350922491]
  We propose using a theoretically guaranteed noisy label detection framework to detect and remove noisy data for Learning with Noisy Labels (LNL) Specifically, we design a penalized regression to model the linear relation between network features and one-hot labels. To make the framework scalable to datasets that contain a large number of categories and training data, we propose a split algorithm to divide the whole training set into small pieces.
  arXiv  Detail & Related papers  (2022-03-15T11:09:58Z)
• Delving into Sample Loss Curve to Embrace Noisy and Imbalanced Data [17.7825114228313]
  Corrupted labels and class imbalance are commonly encountered in practically collected training data. Existing approaches alleviate these issues by adopting a sample re-weighting strategy. However, biased samples with corrupted labels and of tailed classes commonly co-exist in training data.
  arXiv  Detail & Related papers  (2021-12-30T09:20:07Z)
• No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data [78.69828864672978]
  A central challenge in training classification models in the real-world federated system is learning with non-IID data. We propose a novel and simple algorithm called Virtual Representations (CCVR), which adjusts the classifier using virtual representations sampled from an approximated ssian mixture model. Experimental results demonstrate that CCVR state-of-the-art performance on popular federated learning benchmarks including CIFAR-10, CIFAR-100, and CINIC-10.
  arXiv  Detail & Related papers  (2021-06-09T12:02:29Z)

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.