Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T20:35:35.503119Z
Paper Citation Record · LEDGER
An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification
As of 18 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2505.12581.
A citation records a reference. It does not transfer a finding from one paper to another.
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One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
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Source: cited_works
Reference resolution
46 of 46 outbound references displayed
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External citation measurements
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Outbound references
Observation e748817b-46cc-4340-8e3f-e319abf9a3bc · outbound
This paper cites Multiscale context features for geological image classification.
An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification Multiscale context features for geological image classification
Reference 1
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Observation 81fc20ca-a6b8-494c-ae97-7eb05dcbadc7 · outbound
This paper cites Multiscale patch-based feature graphs for image classification.
An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification Multiscale patch-based feature graphs for image classification
Reference 2
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This paper cites Todescato, and Joel Lu \' s Carbonera.
An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification Todescato, and Joel Lu \' s Carbonera
Reference 3
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Observation cdea501a-c720-41a1-8c60-9d9ba5327a9e · outbound
This paper cites Analyzing Effects of Mixed Sample Data Augmentation on Model Interpretability.
An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification Analyzing Effects of Mixed Sample Data Augmentation on Model Interpretability
Reference 4
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Observation b9bd2455-92c1-4dc5-8920-e04c62c659aa · outbound
This paper cites A survey on image data augmentation for deep learning.
An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification A survey on image data augmentation for deep learning
Reference 5
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Observation 78f9ba05-ad5e-499b-a6c1-5b97ec882b91 · outbound
This paper cites Towards explainable deep neural networks (xdnn).
An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification Towards explainable deep neural networks (xdnn)
Reference 6
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Observation 5a9b4209-c466-41f8-bd6d-d07a64b68890 · outbound
This paper cites A survey on neural network interpretability.
An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification A survey on neural network interpretability
Reference 7
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Observation df30c7df-4be1-4212-9237-34637ef06379 · outbound
This paper cites Interpretable deep convolutional neural networks via meta-learning.
An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification Interpretable deep convolutional neural networks via meta-learning
Reference 8
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Observation cff99d11-0dca-47c9-a522-7633cb14204d · outbound
This paper cites An analysis of explainability methods for convolutional neural networks.
An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification An analysis of explainability methods for convolutional neural networks
Reference 9
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Observation 67b047e0-eacb-4d2c-afc7-9ae8db1a444b · outbound
This paper cites On interpretability of artificial neural networks: A survey.
An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification On interpretability of artificial neural networks: A survey
Reference 10
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Observation 348f6e6e-ffd2-44ce-8092-72328c676885 · outbound
This paper cites Eigen-cam: Class activation map using principal components.
An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification Eigen-cam: Class activation map using principal components
Reference 11
- Resolution
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Source-reported events for the cited work
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Observation 5ab44d26-063e-4c28-9dc7-c8ade899b7d8 · outbound
This paper cites The building blocks of interpretability.
An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification The building blocks of interpretability
Reference 12
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Observation 61d23c0b-9291-401a-a5a7-1ce9817de63b · outbound
This paper cites Explaining the effect of data augmentation on image classification tasks, 2020.
An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification Explaining the effect of data augmentation on image classification tasks, 2020
Reference 13
- Resolution
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Observation 4187b5f5-f038-4d45-859a-9174d1335348 · outbound
This paper cites On the impact of interpretability methods in active image augmentation method.
An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification On the impact of interpretability methods in active image augmentation method
Reference 14
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Observation badcec9c-9e66-42a0-9777-8c8ca5494331 · outbound
This paper cites Comparing data augmentation strategies for deep image classification.
An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification Comparing data augmentation strategies for deep image classification
Reference 15
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Observation de25ef23-d2e4-4c87-9148-97a0ad33f663 · outbound
This paper cites Comparison of different image data augmentation approaches.
An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification Comparison of different image data augmentation approaches
Reference 16
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Observation d04a6212-e5a3-436d-9188-3a8a2675f5dc · outbound
This paper cites The Effectiveness of Data Augmentation in Image Classification using Deep Learning.
An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification The Effectiveness of Data Augmentation in Image Classification using Deep Learning
Reference 17
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Observation 47bed8c2-c69c-4f78-b71e-7f443fa323e6 · outbound
This paper cites Data augmentation for hyperspectral image classification with deep cnn.
An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification Data augmentation for hyperspectral image classification with deep cnn
Reference 18
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Observation 9241034c-85ef-4020-9b95-2100c70bc229 · outbound
This paper cites GridMask Data Augmentation.
An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification GridMask Data Augmentation
Reference 19
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This paper cites A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainability.
An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainability
Reference 20
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An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification SaliencyMix: A Saliency Guided Data Augmentation Strategy for Better Regularization
Reference 21
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Observation b9fde2e7-1535-435e-a778-e0c637fd75dd · outbound
This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization.
An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification Grad-cam: Visual explanations from deep networks via gradient-based localization
Reference 22
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An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification Learning multiple layers of features from tiny images
Reference 23
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Observation d1c0c8fe-4675-4bf1-bbea-010435e4c1b1 · outbound
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An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification Efficientnet: Rethinking model scaling for convolutional neural networks
Reference 24
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An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification Introduction to machine learning
Reference 25
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An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification Machine learning: a probabilistic perspective
Reference 26
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An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification O'Reilly Media, Inc
Reference 27
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Observation b3faba8e-ce05-465c-8b3b-99e84bf77a55 · outbound
This paper cites Artificial Intelligence: A Modern Approach (4th Edition).
An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification Artificial Intelligence: A Modern Approach (4th Edition)
Reference 28
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Reference 29
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Observation e9a7327b-454e-476b-ad2f-7ab7aeb0c6bb · outbound
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An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification A comprehensive survey of recent trends in deep learning for digital images augmentation
Reference 30
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Observation 856e2a63-b6bd-4517-87d0-3ae3e8256007 · outbound
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An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification Unresolved cited work
Reference 31
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An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification Learning deep features for discriminative localization
Reference 32
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An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification Score-cam: Score-weighted visual explanations for convolutional neural networks
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An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification Layercam: Exploring hierarchical class activation maps for localization
Reference 34
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An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification Restricting the Flow: Information Bottlenecks for Attribution
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An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification Patchnet: Interpretable Neural Networks for Image Classification
Reference 36
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An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification Image Data Augmentation for Deep Learning: A Survey
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An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification Data Augmentation by Pairing Samples for Images Classification
Reference 38
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An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification Autoaugment: Learning augmentation strategies from data
Reference 39
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An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification Learning to compose domain-specific transformations for data augmentation
Reference 40
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Observation 4a3f779d-4fcf-41ff-8e24-ea9553e18cfa · outbound
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An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification Very deep convolutional neural network based image classification using small training sample size
Reference 41
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An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification A study on cnn transfer learning for image classification
Reference 42
- Resolution
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Observation 4a72b276-38de-40fd-9a5a-e76acf527d37 · outbound
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An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification Deep residual learning for image recognition
Reference 43
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An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification Densely connected convolutional networks
Reference 44
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This paper cites What do different evaluation metrics tell us about saliency models? IEEE transactions on pattern analysis and machine intelligence, 41 0 (3): 0 740--757, 2018.
An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification What do different evaluation metrics tell us about saliency models? IEEE transactions on pattern analysis and machine intelligence, 41 0 (3): 0 740--757, 2018
Reference 45
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Observation 17afba1a-ea01-4b91-ba91-498209ad1b1a · outbound
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An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification Generalized intersection over union: A metric and a loss for bounding box regression
Reference 46
- Resolution
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Pith citing papers
No inbound Pith citation observations are available.