Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification | Paper Citation Record

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This paper cites Contextual diversity for active learning.

Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Contextual diversity for active learning

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This paper cites Active learning: A sur- vey.

Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Active learning: A sur- vey

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This paper cites Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds.

Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds

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This paper cites Training con- nectionist networks with queries and selective sampling.Ad- vances in neural information processing systems, 2, 1989.

Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Training con- nectionist networks with queries and selective sampling.Ad- vances in neural information processing systems, 2, 1989

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This paper cites Gener- alized coverage for more robust low-budget active learning.

Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Gener- alized coverage for more robust low-budget active learning

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This paper cites Uncertainty Herding: One Active Learning Method for All Label Budgets.

Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Uncertainty Herding: One Active Learning Method for All Label Budgets

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This paper cites Mar- gin based active learning.

Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Mar- gin based active learning

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Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Food-101–mining discriminative components with random forests

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This paper cites Se- quential graph convolutional network for active learning.

Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Se- quential graph convolutional network for active learning

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This paper cites Emerg- ing properties in self-supervised vision transformers.

Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Emerg- ing properties in self-supervised vision transformers

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This paper cites Debiased active learning with variational gradient rectifier.

Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Debiased active learning with variational gradient rectifier

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This paper cites A Novel Plug-in Module for Fine-Grained Visual Classification.

Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification A Novel Plug-in Module for Fine-Grained Visual Classification

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This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

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This paper cites Patchup: A feature-space block-level regularization technique for convo- lutional neural networks.

Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Patchup: A feature-space block-level regularization technique for convo- lutional neural networks

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This paper cites Learning gener- ative visual models from few training examples: An incre- mental bayesian approach tested on 101 object categories.

Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Learning gener- ative visual models from few training examples: An incre- mental bayesian approach tested on 101 object categories

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This paper cites Deep bayesian active learning with image data.

Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Deep bayesian active learning with image data

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This paper cites Feature mixing-based active learn- ing for multi-label text classification.

Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Feature mixing-based active learn- ing for multi-label text classification

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This paper cites Balque: Batch active learning by querying unstable examples with calibrated confidence.Pattern Recognition, 151:110385,.

Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Balque: Batch active learning by querying unstable examples with calibrated confidence.Pattern Recognition, 151:110385,

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This paper cites Deep residual learning for image recognition.

Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Deep residual learning for image recognition

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This paper cites Multi-class active learning for image classification.

Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Multi-class active learning for image classification

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This paper cites Novel dataset for fine-grained image categorization: Stanford dogs.

Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Novel dataset for fine-grained image categorization: Stanford dogs

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This paper cites Task-aware variational adversarial active learning.

Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Task-aware variational adversarial active learning

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This paper cites Adam: A Method for Stochastic Optimization.

Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Adam: A Method for Stochastic Optimization

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This paper cites Learning active learning from data.Advances in neural in- formation processing systems, 30, 2017.

Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Learning active learning from data.Advances in neural in- formation processing systems, 30, 2017

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Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Tidal: Learning training dynamics for active learning

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This paper cites A survey on deep active learning: Recent advances and new frontiers.IEEE Trans- actions on Neural Networks and Learning Systems, 36(4): 5879–5899, 2024.

Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification A survey on deep active learning: Recent advances and new frontiers.IEEE Trans- actions on Neural Networks and Learning Systems, 36(4): 5879–5899, 2024

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This paper cites Deep active 9 learning with noise stability.

Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Deep active 9 learning with noise stability

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This paper cites A survey on active deep learning: From model driven to data driven.ACM Computing Surveys (CSUR), 54(10s): 1–34, 2022.

Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification A survey on active deep learning: From model driven to data driven.ACM Computing Surveys (CSUR), 54(10s): 1–34, 2022

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Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Influence selection for active learning

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Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Unresolved cited work

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Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Visualizing data using t-sne.Journal of machine learning research, 9 (Nov):2579–2605, 2008

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Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Instance-wise supervision- level optimization in active learning

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Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Deep deterministic un- certainty: A new simple baseline

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Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Automated flower classification over a large number of classes

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Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Cats and dogs

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Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Active learning by feature mixing

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Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Automatic differentiation in pytorch

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Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification A survey of deep active learning.ACM computing surveys (CSUR), 54(9):1–40, 2021

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Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Margin-based active learning for structured output spaces

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Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Active learning for vision- language models

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Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Entropic open-set active learning

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Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Active Learning for Convolutional Neural Networks: A Core-Set Approach

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Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Active learning literature survey

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Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Vari- ational adversarial active learning

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Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification The caltech-ucsd birds-200-2011 dataset

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Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Mul- tiple instance differentiation learning for active object detec- tion.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(10):12133–12147, 2023

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Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification A survey of dataset refinement for problems in com- puter vision datasets.ACM computing surveys, 56(7):1–34,

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Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification A new active labeling method for deep learning

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Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Active Learning in Bayesian Neural Networks with Balanced Entropy Learning Principle

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This paper cites Covid-al: The diagnosis of covid-19 with deep ac- tive learning.Medical Image Analysis, 68:101913, 2021.

Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Covid-al: The diagnosis of covid-19 with deep ac- tive learning.Medical Image Analysis, 68:101913, 2021

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Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Active learning for domain adaptation: An energy-based approach

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Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Active finetuning: Exploiting an- notation budget in the pretraining-finetuning paradigm

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This paper cites Active learning through a covering lens.Ad- vances in Neural Information Processing Systems, 35: 22354–22367, 2022.

Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Active learning through a covering lens.Ad- vances in Neural Information Processing Systems, 35: 22354–22367, 2022

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Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Learning loss for ac- tive learning

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This paper cites Data- centric artificial intelligence: A survey.ACM Computing Surveys, 57(5):1–42, 2025.

Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Data- centric artificial intelligence: A survey.ACM Computing Surveys, 57(5):1–42, 2025

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Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification A Comparative Survey of Deep Active Learning

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This paper cites State-relabeling adversar- ial active learning.

Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification State-relabeling adversar- ial active learning

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Observation 2c2419dc-105f-4c4c-8dd3-09be6d9e246c · outbound

This paper cites Downstream-pretext domain knowledge traceback for active learning.IEEE Transactions on Multi- media, 26:10585–10596, 2024.

Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Downstream-pretext domain knowledge traceback for active learning.IEEE Transactions on Multi- media, 26:10585–10596, 2024

Reference 58

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This paper cites Employing feature mixture for active learning of object detection.Neurocomputing, 594:127883, 2024.

Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Employing feature mixture for active learning of object detection.Neurocomputing, 594:127883, 2024

Reference 59

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Observation a38656ec-8c25-4667-9005-601c78ca51bb · outbound

This paper cites Multi-granularity archaeological dating of chinese bronze dings based on a knowledge-guided re- lation graph.

Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Multi-granularity archaeological dating of chinese bronze dings based on a knowledge-guided re- lation graph

Reference 60

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Observation b7c1b0c2-588e-4d78-a181-d7b80c582567 · outbound

This paper cites Rethinking epis- temic and aleatoric uncertainty for active open-set annota- tion: An energy-based approach.

Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Rethinking epis- temic and aleatoric uncertainty for active open-set annota- tion: An energy-based approach

Reference 61

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Observation 0b4b5011-8bbe-4bed-a54e-c69609cef991 · outbound

This paper cites w/o Weighted.

Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification w/o Weighted

Reference 62

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