Ask-n-Learn: Active Learning via Reliable Gradient Representations for Image Classification

• URL: http://arxiv.org/abs/2009.14448v1
• Date: Wed, 30 Sep 2020 05:19:56 GMT
• Title: Ask-n-Learn: Active Learning via Reliable Gradient Representations for Image Classification
• Authors: Bindya Venkatesh and Jayaraman J. Thiagarajan
• Abstract summary: Deep predictive models rely on human supervision in the form of labeled training data. We propose Ask-n-Learn, an active learning approach based on gradient embeddings obtained using the pesudo-labels estimated in each of the algorithm.
• Score: 29.43017692274488
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep predictive models rely on human supervision in the form of labeled training data. Obtaining large amounts of annotated training data can be expensive and time consuming, and this becomes a critical bottleneck while building such models in practice. In such scenarios, active learning (AL) strategies are used to achieve faster convergence in terms of labeling efforts. Existing active learning employ a variety of heuristics based on uncertainty and diversity to select query samples. Despite their wide-spread use, in practice, their performance is limited by a number of factors including non-calibrated uncertainties, insufficient trade-off between data exploration and exploitation, presence of confirmation bias etc. In order to address these challenges, we propose Ask-n-Learn, an active learning approach based on gradient embeddings obtained using the pesudo-labels estimated in each iteration of the algorithm. More importantly, we advocate the use of prediction calibration to obtain reliable gradient embeddings, and propose a data augmentation strategy to alleviate the effects of confirmation bias during pseudo-labeling. Through empirical studies on benchmark image classification tasks (CIFAR-10, SVHN, Fashion-MNIST, MNIST), we demonstrate significant improvements over state-of-the-art baselines, including the recently proposed BADGE algorithm.

Related papers

• LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model Training [2.762397703396293]
  Loss Prediction Loss with Gradient Norm (LPLgrad) is designed to quantify model uncertainty effectively and improve the accuracy of image classification tasks. LPLgrad operates in two distinct phases: (i) em Training Phase aims to predict the loss for input features by jointly training a main model and an auxiliary model. This dual-model approach enhances the ability to extract complex input features and learn intrinsic patterns from the data effectively.
  arXiv  Detail & Related papers  (2024-11-20T18:12:59Z)
• Active Learning to Guide Labeling Efforts for Question Difficulty Estimation [1.0514231683620516]
  Transformer-based neural networks achieve state-of-the-art performance, primarily through supervised methods but with an isolated study in unsupervised learning. This work bridges the research gap by exploring active learning for QDE, a supervised human-in-the-loop approach. Experiments demonstrate that active learning with PowerVariance acquisition achieves a performance close to fully supervised models after labeling only 10% of the training data.
  arXiv  Detail & Related papers  (2024-09-14T02:02:42Z)
• Unsupervised Transfer Learning via Adversarial Contrastive Training [3.227277661633986]
  We propose a novel unsupervised transfer learning approach using adversarial contrastive training (ACT) Our experimental results demonstrate outstanding classification accuracy with both fine-tuned linear probe and K-NN protocol across various datasets.
  arXiv  Detail & Related papers  (2024-08-16T05:11:52Z)
• Downstream-Pretext Domain Knowledge Traceback for Active Learning [138.02530777915362]
  We propose a downstream-pretext domain knowledge traceback (DOKT) method that traces the data interactions of downstream knowledge and pre-training guidance. DOKT consists of a traceback diversity indicator and a domain-based uncertainty estimator. Experiments conducted on ten datasets show that our model outperforms other state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-07-20T01:34:13Z)
• NTKCPL: Active Learning on Top of Self-Supervised Model by Estimating True Coverage [3.4806267677524896]
  We propose a novel active learning strategy, neural tangent kernel clustering-pseudo-labels (NTKCPL) It estimates empirical risk based on pseudo-labels and the model prediction with NTK approximation. We validate our method on five datasets, empirically demonstrating that it outperforms the baseline methods in most cases.
  arXiv  Detail & Related papers  (2023-06-07T01:43:47Z)
• Temporal Output Discrepancy for Loss Estimation-based Active Learning [65.93767110342502]
  We present a novel deep active learning approach that queries the oracle for data annotation when the unlabeled sample is believed to incorporate high loss. Our approach achieves superior performances than the state-of-the-art active learning methods on image classification and semantic segmentation tasks.
  arXiv  Detail & Related papers  (2022-12-20T19:29:37Z)
• SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learning [168.89470249446023]
  We present SURF, a semi-supervised reward learning framework that utilizes a large amount of unlabeled samples with data augmentation. In order to leverage unlabeled samples for reward learning, we infer pseudo-labels of the unlabeled samples based on the confidence of the preference predictor. Our experiments demonstrate that our approach significantly improves the feedback-efficiency of the preference-based method on a variety of locomotion and robotic manipulation tasks.
  arXiv  Detail & Related papers  (2022-03-18T16:50:38Z)
• Knowledge-driven Active Learning [70.37119719069499]
  Active learning strategies aim at minimizing the amount of labelled data required to train a Deep Learning model. Most active strategies are based on uncertain sample selection, and even often restricted to samples lying close to the decision boundary. Here we propose to take into consideration common domain-knowledge and enable non-expert users to train a model with fewer samples.
  arXiv  Detail & Related papers  (2021-10-15T06:11:53Z)
• Towards Reducing Labeling Cost in Deep Object Detection [61.010693873330446]
  We propose a unified framework for active learning, that considers both the uncertainty and the robustness of the detector. Our method is able to pseudo-label the very confident predictions, suppressing a potential distribution drift.
  arXiv  Detail & Related papers  (2021-06-22T16:53:09Z)
• Low-Regret Active learning [64.36270166907788]
  We develop an online learning algorithm for identifying unlabeled data points that are most informative for training. At the core of our work is an efficient algorithm for sleeping experts that is tailored to achieve low regret on predictable (easy) instances.
  arXiv  Detail & Related papers  (2021-04-06T22:53:45Z)
• Active and Incremental Learning with Weak Supervision [7.2288756536476635]
  In this work, we describe combinations of an incremental learning scheme and methods of active learning. An object detection task is evaluated in a continuous exploration context on the PASCAL VOC dataset. We also validate a weakly supervised system based on active and incremental learning in a real-world biodiversity application.
  arXiv  Detail & Related papers  (2020-01-20T13:21:14Z)

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.