Deep Bayesian Active Learning, A Brief Survey on Recent Advances

Amindavar, Hamidreza

doi:10.48550/arxiv.2012.08044

Cited by 4 publications

(4 citation statements)

References 14 publications

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“…Several methods for uncertainty estimation have been used in active learning workflows such as entropy [43,47,49], empirical standard deviation with bootstrapping [53], Bayesian uncertainty estimation [54], least confidence [52], margin sampling [55], and mutual information [43]. In this work, the uncertainty is estimated by computing the standard deviation of prediction values generated from the ensemble of models.…”

Section: Discussionmentioning

confidence: 99%

A Comprehensive Investigation of Active Learning Strategies for Conducting Anti-Cancer Drug Screening

Vasanthakumari,

Zhu,

Brettin

et al. 2024

Cancers

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It is well-known that cancers of the same histology type can respond differently to a treatment. Thus, computational drug response prediction is of paramount importance for both preclinical drug screening studies and clinical treatment design. To build drug response prediction models, treatment response data need to be generated through screening experiments and used as input to train the prediction models. In this study, we investigate various active learning strategies of selecting experiments to generate response data for the purposes of (1) improving the performance of drug response prediction models built on the data and (2) identifying effective treatments. Here, we focus on constructing drug-specific response prediction models for cancer cell lines. Various approaches have been designed and applied to select cell lines for screening, including a random, greedy, uncertainty, diversity, combination of greedy and uncertainty, sampling-based hybrid, and iteration-based hybrid approach. All of these approaches are evaluated and compared using two criteria: (1) the number of identified hits that are selected experiments validated to be responsive, and (2) the performance of the response prediction model trained on the data of selected experiments. The analysis was conducted for 57 drugs and the results show a significant improvement on identifying hits using active learning approaches compared with the random and greedy sampling method. Active learning approaches also show an improvement on response prediction performance for some of the drugs and analysis runs compared with the greedy sampling method.

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Section: Discussionmentioning

confidence: 99%

A Comprehensive Investigation of Active Learning Strategies for Conducting Anti-Cancer Drug Screening

Vasanthakumari,

Zhu,

Brettin

et al. 2024

Cancers

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“…On the other hand, sequential training of such expressive models as well as extending the framework to high dimensional data injects even more complexity [4,5,6]. This challenge was relatively underexplored, until a breakthrough work by Gal et al [7], which essentially considered the problem of incorporating deep learning into AL for high dimensional data as highly connected with that of uncertainty representation [8]. They thus approached the problem from the perspective of uncertainty representation in deep learning for AL, and developed a Bayesian AL framework for image data.…”

Section: Introductionmentioning

confidence: 99%

Deep Active Ensemble Sampling For Image Classification

Doretto¹,

Adjeroh²

2022

Preprint

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Conventional active learning (AL) frameworks aim to reduce the cost of data annotation by actively requesting the labeling for the most informative data points. However, introducing AL to data hungry deep learning algorithms has been a challenge. Some proposed approaches include uncertainty-based techniques, geometric methods, implicit combination of uncertainty-based and geometric approaches, and more recently, frameworks based on semi/self supervised techniques. In this paper, we address two specific problems in this area. The first is the need for efficient exploitation/exploration trade-off in sample selection in AL. For this, we present an innovative integration of recent progress in both uncertainty-based and geometric frameworks to enable an efficient exploration/exploitation trade-off in sample selection strategy. To this end, we build on a computationally efficient approximate of Thompson sampling with key changes as a posterior estimator for uncertainty representation. Our framework provides two advantages: (1) accurate posterior estimation, and ( 2) tune-able trade-off between computational overhead and higher accuracy. The second problem is the need for improved training protocols in deep AL. For this, we use ideas from semi/self supervised learning to propose a general approach that is independent of the specific AL technique being used. Taken these together, our framework shows a significant improvement over the state-of-the-art, with results that are comparable to the performance of supervised-learning under the same setting. We show empirical results of our framework, and comparative performance with the state-of-the-art on four datasets, namely, MNIST, CIFAR10, CIFAR100 and ImageNet to establish a new baseline in two different settings.

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“…One major concern in existing model-based approaches [1], [2] is how to capture the appropriate prior information about the complex structure of the underlying tissues and how to incorporate this prior knowledge into the image reconstruction scheme. By the advent of deep neural networks (DNNs) [3], various end-to-end learning-based methods [4][5][6][7][8] have been proposed which try to learn both the physical model and the prior information about the underlying tissues. These methods lead to many shortcomings such as very large number of training pairs requirements and no-guaranteed solutions consistent with the true physical models.…”

Section: Introductionmentioning

confidence: 99%

Regularization by Adversarial Learning for Ultrasound Elasticity Imaging

Mohammadi¹,

Doyley²,

Çetin³

2021

Preprint

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Classical model-based imaging methods for ultrasound elasticity inverse problem require prior constraints about the underlying elasticity patterns, while finding the appropriate hand-crafted prior for each tissue type is a challenge. In contrast, standard data-driven methods count solely on supervised learning on the training data pairs leading to massive network parameters for unnecessary physical model relearning which might not be consistent with the governing physical models of the imaging system. Fusing the physical forward model and noise statistics with data-adaptive priors leads to a united reconstruction framework that guarantees the learned reconstruction agrees with the physical models while coping with the limited training data. In this paper, we propose a new methodology for estimating the elasticity image by solving a regularized optimization problem which benefits from the physics-based modeling via a data-fidelity term and adversarially learned priors via a regularization term. In this method, the regularizer is trained based on the Wasserstein Generative Adversarial Network (WGAN) objective function which tries to distinguish the distribution of clean and noisy images. Leveraging such an adversarial regularizer for parameterizing the distribution of latent images and using gradient descent (GD) for solving the corresponding regularized optimization task leads to stability and convergence of the reconstruction compared to pixel-wise supervised learning schemes. Our simulation results verify the effectiveness and robustness of the proposed methodology with limited training datasets.

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Deep Bayesian Active Learning, A Brief Survey on Recent Advances

Cited by 4 publications

References 14 publications

A Comprehensive Investigation of Active Learning Strategies for Conducting Anti-Cancer Drug Screening

A Comprehensive Investigation of Active Learning Strategies for Conducting Anti-Cancer Drug Screening

Deep Active Ensemble Sampling For Image Classification

Regularization by Adversarial Learning for Ultrasound Elasticity Imaging

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