Weakly Supervised Label Learning Flows

• URL: http://arxiv.org/abs/2302.09649v3
• Date: Mon, 25 Nov 2024 01:03:17 GMT
• Title: Weakly Supervised Label Learning Flows
• Authors: You Lu, Wenzhuo Song, Chidubem Arachie, Bert Huang,
• Abstract summary: We develop label learning flows (LLF), a general framework for weakly supervised learning problems. Our method is a generative model based on normalizing flows. Experiment results show that our method outperforms many baselines we compare against.
• Score: 8.799674132085931
• License:
• Abstract: Supervised learning usually requires a large amount of labelled data. However, attaining ground-truth labels is costly for many tasks. Alternatively, weakly supervised methods learn with cheap weak signals that only approximately label some data. Many existing weakly supervised learning methods learn a deterministic function that estimates labels given the input data and weak signals. In this paper, we develop label learning flows (LLF), a general framework for weakly supervised learning problems. Our method is a generative model based on normalizing flows. The main idea of LLF is to optimize the conditional likelihoods of all possible labelings of the data within a constrained space defined by weak signals. We develop a training method for LLF that trains the conditional flow inversely and avoids estimating the labels. Once a model is trained, we can make predictions with a sampling algorithm. We apply LLF to three weakly supervised learning problems. Experiment results show that our method outperforms many baselines we compare against.

Related papers

• Learning from True-False Labels via Multi-modal Prompt Retrieving [4.940676168993664]
  We propose a novel weakly supervised labeling setting, namely True-False Labels (TFLs) which can achieve high accuracy when generated by vision-language models (VLMs) We theoretically derive a risk-consistent estimator to explore and utilize the conditional probability distribution information of TFLs. Experimental results demonstrate the effectiveness of the proposed TFL setting and MRP learning method.
  arXiv  Detail & Related papers  (2024-05-24T05:39:15Z)
• Boosting Semi-Supervised Learning by bridging high and low-confidence predictions [4.18804572788063]
  Pseudo-labeling is a crucial technique in semi-supervised learning (SSL) We propose a new method called ReFixMatch, which aims to utilize all of the unlabeled data during training.
  arXiv  Detail & Related papers  (2023-08-15T00:27:18Z)
• Robust Positive-Unlabeled Learning via Noise Negative Sample Self-correction [48.929877651182885]
  Learning from positive and unlabeled data is known as positive-unlabeled (PU) learning in literature. We propose a new robust PU learning method with a training strategy motivated by the nature of human learning.
  arXiv  Detail & Related papers  (2023-08-01T04:34:52Z)
• 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)
• All Points Matter: Entropy-Regularized Distribution Alignment for Weakly-supervised 3D Segmentation [67.30502812804271]
  Pseudo-labels are widely employed in weakly supervised 3D segmentation tasks where only sparse ground-truth labels are available for learning. We propose a novel learning strategy to regularize the generated pseudo-labels and effectively narrow the gaps between pseudo-labels and model predictions.
  arXiv  Detail & Related papers  (2023-05-25T08:19:31Z)
• Losses over Labels: Weakly Supervised Learning via Direct Loss Construction [71.11337906077483]
  Programmable weak supervision is a growing paradigm within machine learning. We propose Losses over Labels (LoL) as it creates losses directly from ofs without going through the intermediate step of a label. We show that LoL improves upon existing weak supervision methods on several benchmark text and image classification tasks.
  arXiv  Detail & Related papers  (2022-12-13T22:29:14Z)
• Learned Label Aggregation for Weak Supervision [8.819582879892762]
  We propose a data programming approach that aggregates weak supervision signals to generate labeled data easily. The quality of the generated labels depends on a label aggregation model that aggregates all noisy labels from all LFs to infer the ground-truth labels. We show the model can be trained using synthetically generated data and design an effective architecture for the model.
  arXiv  Detail & Related papers  (2022-07-27T14:36:35Z)
• Data Consistency for Weakly Supervised Learning [15.365232702938677]
  Training machine learning models involves using large amounts of human-annotated data. We propose a novel weak supervision algorithm that processes noisy labels, i.e., weak signals. We show that it significantly outperforms state-of-the-art weak supervision methods on both text and image classification tasks.
  arXiv  Detail & Related papers  (2022-02-08T16:48:19Z)
• Robust Long-Tailed Learning under Label Noise [50.00837134041317]
  This work investigates the label noise problem under long-tailed label distribution. We propose a robust framework,algo, that realizes noise detection for long-tailed learning. Our framework can naturally leverage semi-supervised learning algorithms to further improve the generalisation.
  arXiv  Detail & Related papers  (2021-08-26T03:45:00Z)
• In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning [53.1047775185362]
  Pseudo-labeling (PL) is a general SSL approach that does not have this constraint but performs relatively poorly in its original formulation. We argue that PL underperforms due to the erroneous high confidence predictions from poorly calibrated models. We propose an uncertainty-aware pseudo-label selection (UPS) framework which improves pseudo labeling accuracy by drastically reducing the amount of noise encountered in the training process.
  arXiv  Detail & Related papers  (2021-01-15T23:29:57Z)
• Constrained Labeling for Weakly Supervised Learning [15.365232702938677]
  We propose a simple data-free approach for combining weak supervision signals. Our method is efficient and stable, converging after a few iterations of descent. We show experimentally that our method outperforms other weak supervision methods on various text- and image-classification tasks.
  arXiv  Detail & Related papers  (2020-09-15T21:30:53Z)

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.