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Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization
[article]
2021
arXiv
pre-print
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Feature visualizations such as synthetic maximally activating images are a widely used explanation method to better understand the information processing of convolutional neural networks (CNNs). ...
In summary, synthetic images from a popular feature visualization method are significantly less informative for assessing CNN activations than natural images. ...
Moreover, we thank Leon Sixt for valuable feedback on the introduction and related work. ...
arXiv:2010.12606v3
fatcat:hugtju4inzbsjc7in73ruyw2oy
Deep Learning Based High-Resolution Remote Sensing Image classification
2017
International Journal of Advanced Research in Computer Science and Software Engineering
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In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural ...
It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. ...
, beating the state-of-the-art in certain areas [11] . ...
doi:10.23956/ijarcsse.v7i10.384
fatcat:z4ngztybifdtndb4sdogpxks4q
How Well do Feature Visualizations Support Causal Understanding of CNN Activations?
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2106.12447v2 fatcat:y6aqqxpemvb5fex5qero6sttxiOther Versions
unit-level interpretability methods for humans, and find no evidence that a widely-used feature visualization method provides humans with better "causal understanding" of unit activations than simple alternative ...
These synthetic feature visualizations are purported to provide humans with precise information about the image features that cause a unit to be activated - an advantage over other alternatives like strongly ...
Exemplary natural images explain {cnn} activations better than state-of-the-art feature visualization. In International Conference on Learning Representations, 2021. Cadena, S. A., Weis, M. ...
arXiv:2106.12447v3
fatcat:jg63cxjxpnauplgn5izuhivw4y
MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification
2017
IEEE Geoscience and Remote Sensing Letters
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2017 pre-print arXiv:1612.08879v2 fatcat:7gvbktankfgyrn2enjzeofqunqOther Versions
The classification results on two widely used remote sensing image databases show that the proposed method significantly improves the classification performance compared with other state-of-the-art methods ...
G can produce numerous images that are similar to the training data; therefore, D can learn better representations of remotely sensed images using the training data provided by G. ...
The above-mentioned methods have comprised the state of the art for several years in the remote sensing community [3] , but they are based on hand-crafted features, which are difficult, time-consuming ...
doi:10.1109/lgrs.2017.2752750
fatcat:5upbvzkeqnhq5p3ymdrgivnew4
Imaging Time Series of Eye Tracking Data to Classify Attentional States
2021
Frontiers in Neuroscience
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In current state-of-the-art studies, the extracted eye tracking feature set usually consists of descriptive statistics about specific eye movement characteristics (i.e., fixations, saccades, blinks, vergence ...
The results show that our two-dimensional image features with the convolutional neural net outperform the classical classifiers for most analyses, especially regarding generalization over participants ...
It reaches state-of-the-art accuracy. ...
doi:10.3389/fnins.2021.664490
pmid:34121994
pmcid:PMC8193942
fatcat:xxj2brryrzchtgobfjhyg6urmm
Intelligent 3D Network Protocol for Multimedia Data Classification using Deep Learning
[article]
2022
arXiv
pre-print
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The results are compared with state-of-the-art frameworks from literature for action recognition on UCF101 with an accuracy of 95%. ...
In this paper, we implement Hybrid Deep Learning Architecture that combines STIP and 3D CNN features to enhance the performance of 3D videos effectively. ...
Acknowledgments: Conflicts of Interest: The authors anonymously declared no conflicts of interest. ...
arXiv:2207.11504v1
fatcat:ls5gtagfsjcuzjkae7dbaa3u4m
Verification of Size Invariance in DNN Activations using Concept Embeddings
[article]
2021
arXiv
pre-print
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Its practical applicability is then demonstrated on a new concept dataset by two exemplary assessments of three standard networks, including the larger Mask R-CNN model (arXiv:1703.06870): (1) the consistency ...
of body part similarity, and (2) the invariance of internal representations of body parts with respect to the size in pixels of the depicted person. ...
Concretely, we uncovered some severe performance limitations of Net2Vec that impede application to state-of-the-art sized DNNs or larger concept datasets than the original Broden dataset proposed in [ ...
arXiv:2105.06727v1
fatcat:tojmd4v2o5avfhypk7uagyesdq
Neuroscope: An Explainable AI Toolbox for Semantic Segmentation and Image Classification of Convolutional Neural Nets
2021
Applied Sciences
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Our proposed toolbox Neuroscope addresses this demand by offering state-of-the-art visualization algorithms for image classification and newly adapted methods for semantic segmentation of convolutional ...
The need for tools explaining AI for deep learning of images is thus eminent. ...
They are included in our toolbox Neuroscope, thereby offering state-of-the-art visualization algorithms for both image classification and semantic segmentation with CNNs. ...
doi:10.3390/app11052199
doaj:a15331689eac47c084ba2f24941e10d0
fatcat:jqmy5lpbuncfnilqyrhpdgvfre
CNN Variants for Computer Vision: History, Architecture, Application, Challenges and Future Scope
2021
Electronics
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Computer vision is becoming an increasingly trendy word in the area of image processing. ...
Several inspirational concepts for the progress of CNN have been investigated, including alternative activation functions, regularization, parameter optimization, and architectural advances. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/electronics10202470
fatcat:aqhrysjtbjagzl6byalgy2du5a
Deep Learning Based Automated COVID-19 Classification from Computed Tomography Images
[article]
2022
arXiv
pre-print
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Despite the simplicity in architecture, the proposed CNN model showed improved quantitative results exceeding state-of-the-art when predicting slice cases. ...
A method of a Convolutional Neural Networks (CNN) for image classification with image preprocessing and hyperparameters tuning was proposed. ...
ACKNOWLEDGEMENT The authors acknowledge the work of all the medical staff and others who manually annotated the images in the COV19-CT-DB database and shared them in a relatively big dataset. ...
arXiv:2111.11191v5
fatcat:fhgke4rsprdvvkqbnxws75hyiu
Guide Me: Interacting with Deep Networks
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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While much prior work lies at the intersection of natural language and vision, such as image captioning or image generation from text descriptions, less focus has been placed on the use of language to ...
guide or improve the performance of a learned visual processing algorithm. ...
Acknowledgments We would like to thank Robert DiPietro for discussions about the idea and Helen L. ...
doi:10.1109/cvpr.2018.00892
dblp:conf/cvpr/RupprechtLNHT18
fatcat:rmcu7vi2drdkro7lm6xtwyihfy
Guide Me: Interacting with Deep Networks
[article]
2018
arXiv
pre-print
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While much prior work lies at the intersection of natural language and vision, such as image captioning or image generation from text descriptions, less focus has been placed on the use of language to ...
guide or improve the performance of a learned visual processing algorithm. ...
Acknowledgments We would like to thank Robert DiPietro for discussions about the idea and Helen L. ...
arXiv:1803.11544v1
fatcat:czxjztoozvajtppn74vhuildii
VisualBackProp: efficient visualization of CNNs
[article]
2017
arXiv
pre-print
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This paper proposes a new method, that we call VisualBackProp, for visualizing which sets of pixels of the input image contribute most to the predictions made by the convolutional neural network (CNN). ...
than a forward propagation. ...
We empirically demonstrate that it is order of magnitude faster than the state-of-the-art visualization method, layer-wise relevance propagation (LRP) [11] , while at the same time it leads to very similar ...
arXiv:1611.05418v3
fatcat:jezogw3rqre2jor2kqavvuwepu
Understanding auditory representations of emotional expressions with neural networks
2018
Neural computing & applications (Print)
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The aim of this work is to contribute to a deeper understanding of the acoustic and prosodic features that are relevant for the perception of emotional states. ...
Although achieving state-of-the-art performances, it is still not fully understood what these networks learn and how the learned representations correlate with the emotional characteristics of speech. ...
Compliance with ethical standards Conflict of interest The authors declare that they have no conflict of interest. ...
doi:10.1007/s00521-018-3869-3
fatcat:cim2jpoxgrd7ljr6wn74zxtouy
Vision Transformers in Medical Computer Vision – A Contemplative Retrospection
[article]
2022
arXiv
pre-print
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Recent escalation in the field of computer vision underpins a huddle of algorithms with the magnificent potential to unravel the information contained within images. ...
These computer vision algorithms are being practised in medical image analysis and are transfiguring the perception and interpretation of Imaging data. ...
Firstly, the low-level features were extracted using state-of-the-art CNN architectures. ...
arXiv:2203.15269v1
fatcat:wecjpoikbvfz5cygytqpktoxdq
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