alexnet softmax Search Results


alexnet softmax  (SoftMax Inc)
90
SoftMax Inc alexnet softmax
The errors were computed over 3 epochs, of which each has 20000 iterations. Both learning processes used the same local receptive fields and drop ratio. The two loss functions were associated with a large L2-norm weight decay constant 0.005 (larger than that used in <t>AlexNet</t> ), which has proved to be useful for improving generalization of neural networks . Under these settings, softmax and hinge respectively achieved 0.932 and 0.891 in validation accuracy.
Alexnet Softmax, supplied by SoftMax 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/alexnet softmax/product/SoftMax Inc
Average 90 stars, based on 1 article reviews
alexnet softmax - by Bioz Stars, 2026-06
90/100 stars
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alexnet+nnc  (SoftMax Inc)
90
SoftMax Inc alexnet+nnc
The errors were computed over 3 epochs, of which each has 20000 iterations. Both learning processes used the same local receptive fields and drop ratio. The two loss functions were associated with a large L2-norm weight decay constant 0.005 (larger than that used in <t>AlexNet</t> ), which has proved to be useful for improving generalization of neural networks . Under these settings, softmax and hinge respectively achieved 0.932 and 0.891 in validation accuracy.
Alexnet+Nnc, supplied by SoftMax 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/alexnet+nnc/product/SoftMax Inc
Average 90 stars, based on 1 article reviews
alexnet+nnc - by Bioz Stars, 2026-06
90/100 stars
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pretrained alexnet  (SoftMax Inc)
90
SoftMax Inc pretrained alexnet
The errors were computed over 3 epochs, of which each has 20000 iterations. Both learning processes used the same local receptive fields and drop ratio. The two loss functions were associated with a large L2-norm weight decay constant 0.005 (larger than that used in <t>AlexNet</t> ), which has proved to be useful for improving generalization of neural networks . Under these settings, softmax and hinge respectively achieved 0.932 and 0.891 in validation accuracy.
Pretrained Alexnet, supplied by SoftMax 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/pretrained alexnet/product/SoftMax Inc
Average 90 stars, based on 1 article reviews
pretrained alexnet - by Bioz Stars, 2026-06
90/100 stars
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alexnet-emotion network  (SoftMax Inc)
90
SoftMax Inc alexnet-emotion network
The errors were computed over 3 epochs, of which each has 20000 iterations. Both learning processes used the same local receptive fields and drop ratio. The two loss functions were associated with a large L2-norm weight decay constant 0.005 (larger than that used in <t>AlexNet</t> ), which has proved to be useful for improving generalization of neural networks . Under these settings, softmax and hinge respectively achieved 0.932 and 0.891 in validation accuracy.
Alexnet Emotion Network, supplied by SoftMax 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/alexnet-emotion network/product/SoftMax Inc
Average 90 stars, based on 1 article reviews
alexnet-emotion network - by Bioz Stars, 2026-06
90/100 stars
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Image Search Results


The errors were computed over 3 epochs, of which each has 20000 iterations. Both learning processes used the same local receptive fields and drop ratio. The two loss functions were associated with a large L2-norm weight decay constant 0.005 (larger than that used in AlexNet ), which has proved to be useful for improving generalization of neural networks . Under these settings, softmax and hinge respectively achieved 0.932 and 0.891 in validation accuracy.

Journal: Scientific Reports

Article Title: Localization and Classification of Paddy Field Pests using a Saliency Map and Deep Convolutional Neural Network

doi: 10.1038/srep20410

Figure Lengend Snippet: The errors were computed over 3 epochs, of which each has 20000 iterations. Both learning processes used the same local receptive fields and drop ratio. The two loss functions were associated with a large L2-norm weight decay constant 0.005 (larger than that used in AlexNet ), which has proved to be useful for improving generalization of neural networks . Under these settings, softmax and hinge respectively achieved 0.932 and 0.891 in validation accuracy.

Article Snippet: AlexNet , Softmax , 0.834.

Techniques: Biomarker Discovery

Comparison of DCNNs with other methods on the same dataset.

Journal: Scientific Reports

Article Title: Localization and Classification of Paddy Field Pests using a Saliency Map and Deep Convolutional Neural Network

doi: 10.1038/srep20410

Figure Lengend Snippet: Comparison of DCNNs with other methods on the same dataset.

Article Snippet: AlexNet , Softmax , 0.834.

Techniques: Comparison