Active Learning under Pool Set Distribution Shift and Noisy Data

• URL: http://arxiv.org/abs/2106.11719v1
• Date: Tue, 22 Jun 2021 12:39:30 GMT
• Title: Active Learning under Pool Set Distribution Shift and Noisy Data
• Authors: Andreas Kirsch, Tom Rainforth, Yarin Gal
• Abstract summary: We show that BALD gets stuck on out-of-distribution or junk data that is not relevant for the task. We examine a novel *Expected Predictive Information Gain (EPIG)* to deal with distribution shifts of the pool set. EPIG reduces the uncertainty of *predictions* on an unlabelled *evaluation set* sampled from the test data distribution whose distribution might be different to the pool set distribution.
• Score: 41.69385715445311
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Active Learning is essential for more label-efficient deep learning. Bayesian Active Learning has focused on BALD, which reduces model parameter uncertainty. However, we show that BALD gets stuck on out-of-distribution or junk data that is not relevant for the task. We examine a novel *Expected Predictive Information Gain (EPIG)* to deal with distribution shifts of the pool set. EPIG reduces the uncertainty of *predictions* on an unlabelled *evaluation set* sampled from the test data distribution whose distribution might be different to the pool set distribution. Based on this, our new EPIG-BALD acquisition function for Bayesian Neural Networks selects samples to improve the performance on the test data distribution instead of selecting samples that reduce model uncertainty everywhere, including for out-of-distribution regions with low density in the test data distribution. Our method outperforms state-of-the-art Bayesian active learning methods on high-dimensional datasets and avoids out-of-distribution junk data in cases where current state-of-the-art methods fail.

Related papers

• DOTA: Distributional Test-Time Adaptation of Vision-Language Models [52.98590762456236]
  Training-free test-time dynamic adapter (TDA) is a promising approach to address this issue. We propose a simple yet effective method for DistributiOnal Test-time Adaptation (Dota) Dota continually estimates the distributions of test samples, allowing the model to continually adapt to the deployment environment.
  arXiv  Detail & Related papers  (2024-09-28T15:03:28Z)
• Uncertainty Measurement of Deep Learning System based on the Convex Hull of Training Sets [0.13265175299265505]
  We propose To-hull Uncertainty and Closure Ratio, which measures an uncertainty of trained model based on the convex hull of training data. It can observe the positional relation between the convex hull of the learned data and an unseen sample and infer how extrapolate the sample is from the convex hull.
  arXiv  Detail & Related papers  (2024-05-25T06:25:24Z)
• Deep Active Learning with Contrastive Learning Under Realistic Data Pool Assumptions [2.578242050187029]
  Active learning aims to identify the most informative data from an unlabeled data pool that enables a model to reach the desired accuracy rapidly. Most existing active learning methods have been evaluated in an ideal setting where only samples relevant to the target task exist in an unlabeled data pool. We introduce new active learning benchmarks that include ambiguous, task-irrelevant out-of-distribution as well as in-distribution samples.
  arXiv  Detail & Related papers  (2023-03-25T10:46:10Z)
• Adaptive Negative Evidential Deep Learning for Open-set Semi-supervised Learning [69.81438976273866]
  Open-set semi-supervised learning (Open-set SSL) considers a more practical scenario, where unlabeled data and test data contain new categories (outliers) not observed in labeled data (inliers) We introduce evidential deep learning (EDL) as an outlier detector to quantify different types of uncertainty, and design different uncertainty metrics for self-training and inference. We propose a novel adaptive negative optimization strategy, making EDL more tailored to the unlabeled dataset containing both inliers and outliers.
  arXiv  Detail & Related papers  (2023-03-21T09:07:15Z)
• Bayesian Self-Supervised Contrastive Learning [16.903874675729952]
  This paper proposes a new self-supervised contrastive loss called the BCL loss. The key idea is to design the desired sampling distribution for sampling hard true negative samples under the Bayesian framework. Experiments validate the effectiveness and superiority of the BCL loss.
  arXiv  Detail & Related papers  (2023-01-27T12:13:06Z)
• Pareto Optimization for Active Learning under Out-of-Distribution Data Scenarios [79.02009938011447]
  We propose a sampling scheme, which selects optimal subsets of unlabeled samples with fixed batch size from the unlabeled data pool. Experimental results show its effectiveness on both classical Machine Learning (ML) and Deep Learning (DL) tasks.
  arXiv  Detail & Related papers  (2022-07-04T04:11:44Z)
• CAFA: Class-Aware Feature Alignment for Test-Time Adaptation [50.26963784271912]
  Test-time adaptation (TTA) aims to address this challenge by adapting a model to unlabeled data at test time. We propose a simple yet effective feature alignment loss, termed as Class-Aware Feature Alignment (CAFA), which simultaneously encourages a model to learn target representations in a class-discriminative manner.
  arXiv  Detail & Related papers  (2022-06-01T03:02:07Z)
• Learn from Unpaired Data for Image Restoration: A Variational Bayes Approach [18.007258270845107]
  We propose LUD-VAE, a deep generative method to learn the joint probability density function from data sampled from marginal distributions. We apply our method to real-world image denoising and super-resolution tasks and train the models using the synthetic data generated by the LUD-VAE.
  arXiv  Detail & Related papers  (2022-04-21T13:27:17Z)
• Mind the Trade-off: Debiasing NLU Models without Degrading the In-distribution Performance [70.31427277842239]
  We introduce a novel debiasing method called confidence regularization. It discourages models from exploiting biases while enabling them to receive enough incentive to learn from all the training examples. We evaluate our method on three NLU tasks and show that, in contrast to its predecessors, it improves the performance on out-of-distribution datasets.
  arXiv  Detail & Related papers  (2020-05-01T11:22:55Z)
• Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision [5.352699766206807]
  Active learning (AL) aims to minimize labeling efforts for data-demanding deep neural networks (DNNs) We propose a low-complexity method for feature density matching using self-supervised Fisher kernel (FK) Our method outperforms state-of-the-art methods on MNIST, SVHN, and ImageNet classification while requiring only 1/10th of processing.
  arXiv  Detail & Related papers  (2020-03-01T03:56:32Z)

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.