MCDAL: Maximum Classifier Discrepancy for Active Learning
- URL: http://arxiv.org/abs/2107.11049v1
- Date: Fri, 23 Jul 2021 06:57:08 GMT
- Title: MCDAL: Maximum Classifier Discrepancy for Active Learning
- Authors: Jae Won Cho, Dong-Jin Kim, Yunjae Jung, In So Kweon
- Abstract summary: Recent state-of-the-art active learning methods have mostly leveraged Generative Adversarial Networks (GAN) for sample acquisition.
We propose in this paper a novel active learning framework that we call Maximum Discrepancy for Active Learning (MCDAL)
In particular, we utilize two auxiliary classification layers that learn tighter decision boundaries by maximizing the discrepancies among them.
- Score: 74.73133545019877
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recent state-of-the-art active learning methods have mostly leveraged
Generative Adversarial Networks (GAN) for sample acquisition; however, GAN is
usually known to suffer from instability and sensitivity to hyper-parameters.
In contrast to these methods, we propose in this paper a novel active learning
framework that we call Maximum Classifier Discrepancy for Active Learning
(MCDAL) which takes the prediction discrepancies between multiple classifiers.
In particular, we utilize two auxiliary classification layers that learn
tighter decision boundaries by maximizing the discrepancies among them.
Intuitively, the discrepancies in the auxiliary classification layers'
predictions indicate the uncertainty in the prediction. In this regard, we
propose a novel method to leverage the classifier discrepancies for the
acquisition function for active learning. We also provide an interpretation of
our idea in relation to existing GAN based active learning methods and domain
adaptation frameworks. Moreover, we empirically demonstrate the utility of our
approach where the performance of our approach exceeds the state-of-the-art
methods on several image classification and semantic segmentation datasets in
active learning setups.
Related papers
- MALADY: Multiclass Active Learning with Auction Dynamics on Graphs [0.9831489366502301]
We introduce the Multiclass Active Learning with Auction Dynamics on Graphs (MALADY) framework for efficient active learning.
We generalize the auction dynamics algorithm on similarity graphs for semi-supervised learning in [24] to incorporate a more general optimization functional.
We also introduce a novel active learning acquisition function that uses the dual variable of the auction algorithm to measure the uncertainty in the classifier to prioritize queries near the decision boundaries between different classes.
arXiv Detail & Related papers (2024-09-14T16:20:26Z) - Bayesian Learning-driven Prototypical Contrastive Loss for Class-Incremental Learning [42.14439854721613]
We propose a prototypical network with a Bayesian learning-driven contrastive loss (BLCL) tailored specifically for class-incremental learning scenarios.
Our approach dynamically adapts the balance between the cross-entropy and contrastive loss functions with a Bayesian learning technique.
arXiv Detail & Related papers (2024-05-17T19:49:02Z) - 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) - Synergies between Disentanglement and Sparsity: Generalization and
Identifiability in Multi-Task Learning [79.83792914684985]
We prove a new identifiability result that provides conditions under which maximally sparse base-predictors yield disentangled representations.
Motivated by this theoretical result, we propose a practical approach to learn disentangled representations based on a sparsity-promoting bi-level optimization problem.
arXiv Detail & Related papers (2022-11-26T21:02:09Z) - Consistency-Based Semi-supervised Evidential Active Learning for
Diagnostic Radiograph Classification [2.3545156585418328]
We introduce a novel Consistency-based Semi-supervised Evidential Active Learning framework (CSEAL)
We leverage predictive uncertainty based on theories of evidence and subjective logic to develop an end-to-end integrated approach.
Our approach can substantially improve accuracy on rarer abnormalities with fewer labelled samples.
arXiv Detail & Related papers (2022-09-05T09:28:31Z) - Consistency and Diversity induced Human Motion Segmentation [231.36289425663702]
We propose a novel Consistency and Diversity induced human Motion (CDMS) algorithm.
Our model factorizes the source and target data into distinct multi-layer feature spaces.
A multi-mutual learning strategy is carried out to reduce the domain gap between the source and target data.
arXiv Detail & Related papers (2022-02-10T06:23:56Z) - Discriminative Attribution from Counterfactuals [64.94009515033984]
We present a method for neural network interpretability by combining feature attribution with counterfactual explanations.
We show that this method can be used to quantitatively evaluate the performance of feature attribution methods in an objective manner.
arXiv Detail & Related papers (2021-09-28T00:53:34Z) - Margin Preserving Self-paced Contrastive Learning Towards Domain
Adaptation for Medical Image Segmentation [51.93711960601973]
We propose a novel margin preserving self-paced contrastive Learning model for cross-modal medical image segmentation.
With the guidance of progressively refined semantic prototypes, a novel margin preserving contrastive loss is proposed to boost the discriminability of embedded representation space.
Experiments on cross-modal cardiac segmentation tasks demonstrate that MPSCL significantly improves semantic segmentation performance.
arXiv Detail & Related papers (2021-03-15T15:23:10Z) - Spatial Contrastive Learning for Few-Shot Classification [9.66840768820136]
We propose a novel attention-based spatial contrastive objective to learn locally discriminative and class-agnostic features.
With extensive experiments, we show that the proposed method outperforms state-of-the-art approaches.
arXiv Detail & Related papers (2020-12-26T23:39:41Z) - Spectrum-Guided Adversarial Disparity Learning [52.293230153385124]
We propose a novel end-to-end knowledge directed adversarial learning framework.
It portrays the class-conditioned intraclass disparity using two competitive encoding distributions and learns the purified latent codes by denoising learned disparity.
The experiments on four HAR benchmark datasets demonstrate the robustness and generalization of our proposed methods over a set of state-of-the-art.
arXiv Detail & Related papers (2020-07-14T05:46:27Z)
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.