alexnet dcnn model Search Results


alexnet dcnn model  (MathWorks Inc)
96
MathWorks Inc alexnet dcnn model
a) Decoding emotions from deep convolutional neural network <t>(DCNN)</t> representations using partial least squares regression. b) fc8 layer in the <t>AlexNet</t> model outperformed fc8 layer in EmoNet model in predicting emotion ratings. Each line and dot represent the result of a cross-validated fold. c) AlexNet consistently outperformed EmoNet in predicting emotion ratings across three subsets of BOLD5000 images. d) EmoNet outperformed AlexNet in predicting emotion ratings for the Cowen17 dataset. In contrast, for the BOLD5000 dataset, AlexNet outperformed EmoNet in predicting emotion ratings. Error bars represent the standard error across cross-validated folds.
Alexnet Dcnn Model, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/alexnet dcnn model/product/MathWorks Inc
Average 96 stars, based on 1 article reviews
alexnet dcnn model - by Bioz Stars, 2026-03
96/100 stars
  Buy from Supplier
alexnet  (Thermo Fisher)
90
Thermo Fisher alexnet
a) Decoding emotions from deep convolutional neural network <t>(DCNN)</t> representations using partial least squares regression. b) fc8 layer in the <t>AlexNet</t> model outperformed fc8 layer in EmoNet model in predicting emotion ratings. Each line and dot represent the result of a cross-validated fold. c) AlexNet consistently outperformed EmoNet in predicting emotion ratings across three subsets of BOLD5000 images. d) EmoNet outperformed AlexNet in predicting emotion ratings for the Cowen17 dataset. In contrast, for the BOLD5000 dataset, AlexNet outperformed EmoNet in predicting emotion ratings. Error bars represent the standard error across cross-validated folds.
Alexnet, supplied by Thermo Fisher, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/alexnet/product/Thermo Fisher
Average 90 stars, based on 1 article reviews
alexnet - by Bioz Stars, 2026-03
90/100 stars
  Buy from Supplier
convnet features  (Kaggle Inc)
90
Kaggle Inc convnet features
a) Decoding emotions from deep convolutional neural network <t>(DCNN)</t> representations using partial least squares regression. b) fc8 layer in the <t>AlexNet</t> model outperformed fc8 layer in EmoNet model in predicting emotion ratings. Each line and dot represent the result of a cross-validated fold. c) AlexNet consistently outperformed EmoNet in predicting emotion ratings across three subsets of BOLD5000 images. d) EmoNet outperformed AlexNet in predicting emotion ratings for the Cowen17 dataset. In contrast, for the BOLD5000 dataset, AlexNet outperformed EmoNet in predicting emotion ratings. Error bars represent the standard error across cross-validated folds.
Convnet Features, supplied by Kaggle Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/convnet features/product/Kaggle Inc
Average 90 stars, based on 1 article reviews
convnet features - by Bioz Stars, 2026-03
90/100 stars
  Buy from Supplier
resnet  (Molecular Biosciences Inc)
90
Molecular Biosciences Inc resnet
a) Decoding emotions from deep convolutional neural network <t>(DCNN)</t> representations using partial least squares regression. b) fc8 layer in the <t>AlexNet</t> model outperformed fc8 layer in EmoNet model in predicting emotion ratings. Each line and dot represent the result of a cross-validated fold. c) AlexNet consistently outperformed EmoNet in predicting emotion ratings across three subsets of BOLD5000 images. d) EmoNet outperformed AlexNet in predicting emotion ratings for the Cowen17 dataset. In contrast, for the BOLD5000 dataset, AlexNet outperformed EmoNet in predicting emotion ratings. Error bars represent the standard error across cross-validated folds.
Resnet, supplied by Molecular Biosciences Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/resnet/product/Molecular Biosciences Inc
Average 90 stars, based on 1 article reviews
resnet - by Bioz Stars, 2026-03
90/100 stars
  Buy from Supplier

Image Search Results


a) Decoding emotions from deep convolutional neural network (DCNN) representations using partial least squares regression. b) fc8 layer in the AlexNet model outperformed fc8 layer in EmoNet model in predicting emotion ratings. Each line and dot represent the result of a cross-validated fold. c) AlexNet consistently outperformed EmoNet in predicting emotion ratings across three subsets of BOLD5000 images. d) EmoNet outperformed AlexNet in predicting emotion ratings for the Cowen17 dataset. In contrast, for the BOLD5000 dataset, AlexNet outperformed EmoNet in predicting emotion ratings. Error bars represent the standard error across cross-validated folds.

Journal: bioRxiv

Article Title: Object representations drive emotion schemas across a large and diverse set of daily-life scenes

doi: 10.1101/2025.02.19.638854

Figure Lengend Snippet: a) Decoding emotions from deep convolutional neural network (DCNN) representations using partial least squares regression. b) fc8 layer in the AlexNet model outperformed fc8 layer in EmoNet model in predicting emotion ratings. Each line and dot represent the result of a cross-validated fold. c) AlexNet consistently outperformed EmoNet in predicting emotion ratings across three subsets of BOLD5000 images. d) EmoNet outperformed AlexNet in predicting emotion ratings for the Cowen17 dataset. In contrast, for the BOLD5000 dataset, AlexNet outperformed EmoNet in predicting emotion ratings. Error bars represent the standard error across cross-validated folds.

Article Snippet: The AlexNet DCNN model was implemented via MATLAB’s Deep Learning Toolbox, to extract features from 4913 images.

Techniques:

a) Results of decoding emotions from AlexNet representations. These results show that emotional information is processed hierarchically in a visual object processing system. b) Differences between AlexNet layers in predicting emotion ratings. c) The hierarchical processing of emotional information was consistent across the three subsets of BOLD5000 images. d) The hierarchical processing of emotional information was consistent regardless of whether the BOLD5000 or Cowen17 dataset was used. Error bars represent the standard error across cross-validated folds and each dot represents the result of a cross-validated fold.

Journal: bioRxiv

Article Title: Object representations drive emotion schemas across a large and diverse set of daily-life scenes

doi: 10.1101/2025.02.19.638854

Figure Lengend Snippet: a) Results of decoding emotions from AlexNet representations. These results show that emotional information is processed hierarchically in a visual object processing system. b) Differences between AlexNet layers in predicting emotion ratings. c) The hierarchical processing of emotional information was consistent across the three subsets of BOLD5000 images. d) The hierarchical processing of emotional information was consistent regardless of whether the BOLD5000 or Cowen17 dataset was used. Error bars represent the standard error across cross-validated folds and each dot represents the result of a cross-validated fold.

Article Snippet: The AlexNet DCNN model was implemented via MATLAB’s Deep Learning Toolbox, to extract features from 4913 images.

Techniques:

The representational similarity analysis results indicated that both a) the conv1 layer and b) the fc8 layer of AlexNet exhibited greater within-cluster similarity than between-cluster similarity, suggesting that both layers encode emotional information, regardless of the number of K-means clusters. c) The fc8 layer demonstrated a larger difference in pattern similarity between within-cluster and between-cluster comparisons than the conv1 layer, indicating that the fc8 layer encodes more emotional information. Error bars represent the standard error across clusters and each dot represents the result of a cluster.

Journal: bioRxiv

Article Title: Object representations drive emotion schemas across a large and diverse set of daily-life scenes

doi: 10.1101/2025.02.19.638854

Figure Lengend Snippet: The representational similarity analysis results indicated that both a) the conv1 layer and b) the fc8 layer of AlexNet exhibited greater within-cluster similarity than between-cluster similarity, suggesting that both layers encode emotional information, regardless of the number of K-means clusters. c) The fc8 layer demonstrated a larger difference in pattern similarity between within-cluster and between-cluster comparisons than the conv1 layer, indicating that the fc8 layer encodes more emotional information. Error bars represent the standard error across clusters and each dot represents the result of a cluster.

Article Snippet: The AlexNet DCNN model was implemented via MATLAB’s Deep Learning Toolbox, to extract features from 4913 images.

Techniques: