Review



1 × 1 convolution with the softmax activation function  (SoftMax Inc)

 
  • Logo
  • About
  • News
  • Press Release
  • Team
  • Advisors
  • Partners
  • Contact
  • Bioz Stars
  • Bioz vStars
  • 90

    Structured Review

    SoftMax Inc 1 × 1 convolution with the softmax activation function
    1 × 1 Convolution With The Softmax Activation Function, 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/product/convolution+1/conv+3%C3%973+mbconvl++3%C3%973+mbconv6++3%C3%973+mbconv6++5%C3%975+conv+1%C3%971+pooling+fc/pmc07436155-137-23-26
    Average 90 stars, based on 1 article reviews
    1 × 1 convolution with the softmax activation function - by Bioz Stars, 2026-10
    90/100 stars

    Images

    Related Articles

    other:

    Article Title: Context and detail interaction network for stereo rain streak and raindrop removal.
    Article Snippet: Recently stereo image deraining has attracted lots of attention due to its superiority of abundant information from cross views.. Exploring interaction information across stereo views is the key to improving the performance of stereo image deraining.. In this paper, we design a general coarseto-fine deraining framework for stereo rain streak and raindrop removal, called CDINet, comprising a stereo rain removal subnet and a stereo detail recovery subnet to restore images progressively.

    Article Title: Edge morphology attention mechanism and optimal geometric matching connection model for vascular segmentation
    Article Snippet: Over the past decades, medical image segmentation methods have been intensively developed, but there are still a number of unsolved challenging problems in vascular image segmentation, including broken vessels, insufficient vessel branches, and missing small vessels.. In order to optimize the topology and accuracy of segmented vessels, we propose a novel Edge Morphology Attention Network (EMA-Net) for the segmentation of vessel-like structures, and an Optimal Geometric Matching Connection (OGMC) model to connect the broken vessel fragments.. The EMA-Net has an edge attention module that is able to improve segmented performances of edges and small branches by morphology operation extracting boundary voxels on multi-scales.

    Article Title: BackMix: Regularizing Open Set Recognition by Removing Underlying Fore-Background Priors
    Article Snippet: Open set recognition (OSR) requires models to classify known samples while detecting unknown samples for realworld applications.. Existing studies show impressive progress using unknown samples from auxiliary datasets to regularize OSR models, but they have proved to be sensitive to selecting such known outliers.. In this paper, we discuss the aforementioned problem from a new perspective: Can we regularize OSR models without elaborately selecting auxiliary known outliers?

    Article Title: Multi-LiDAR human joint recognition algorithm in hospital wards based on improved V2V-Posenet
    Article Snippet: The model output heatmap will go through a 3D SoftMax to generate the output key points.

    Article Title: Dataset for Automatic Region-based Coronary Artery Disease Diagnostics Using X-Ray Angiography Images.
    Article Snippet: Conv 1 x 1, Softmax Conv 3 x 3 + + + + Conv 2 x 2 TrConv 2 x 2 ResBlock Input Output 512 x 512 x 1 512 x 512 x 2 512 x 512 x 16 256 x 256 x 32 128 x 128 x 64 64 x 64 x 128 Fig. 2 U-Net Architecture.

    Article Title: Feature deformation network with multi-range feature enhancement for agricultural machinery operation mode identification
    Article Snippet: Short-range features extraction Lon Speed Lat Dir Kinematic equation Acc Lon Lat Dir Speed Angle Speed Angle Ace Extracted short-range features F1Initial features F0 Long-range features extraction Short-range features F1 Sliding window Extracted long-range features F2 Std Aver Med Max Ske Kur Min Trajectory data representation paradigm 1×d m × d Trajectory features (F1 and F2) LatLon Dir ... ... ... Liner projection Trajectory feature map Resize 1×D N N Road field trajectory classification Trajectory feature maps Road Field Conv 3×3 MBConvl, 3×3 MBConv6, 3×3 MBConv6, 5×5 Conv 1×1&Pooling&FC Softmax Classified trajectory Field Road Trajectory feature image generation EfficientNet-B0 Long-range features extraction Short-range features extraction Multi-range feature enhancement Original trajectory 7 × 7 × 1 9 2 7 × 7 × 1 9 2 7 × 7 × 1 9 2 7 × 7 × 1 9 2 7 × 7 × 3 2 0 7 × 7 × 1 2 8 0 1 × 1 × 2 2 8 × 2 8 × 8 0 2 8 × 2 8 × 8 0 2 8 × 2 8 × 8 0 2 8 × 2 8 × 4 0 2 8 × 2 8 × 4 0 5 6 × 5 6 × 2 4 5 6 × 5 6 × 2 4 1 4 × 1 4 × 1 1 2 1 4 × 1 4 × 1 1 2 1 4 × 1 4 × 1 1 2 2 2 4 × 2 2 4 × 3 1 1 2 × 1 1 2 × 3 2 1 1 2 × 1 1 2 × 1 6 Figure 1 The pipeline of the proposed FDRNet

    Article Title: CLRNetV2: A Faster and Stronger Lane Detector
    Article Snippet: Lane is critical in the vision navigation system of intelligent vehicles.. Naturally, the lane is a traffic sign with high-level semantics, whereas it owns the specific local pattern which needs detailed low-level features to localize accurately.. Using different feature levels is of great importance for accurate lane detection, but it is still under-explored.

    Activation Assay:

    Article Title: DEHA -Net: A Dual-Encoder-Based Hard Attention Network with an Adaptive ROI Mechanism for Lung Nodule Segmentation.
    Article Snippet: .. In the last level of the decoder, the upsampling layer was replaced by a convolution layer of a single filter with SoftMax activation. ..



    Similar Products

    86
    Brookhaven Instruments convolutional autoencoder sreevarsha sreejith 1 3 anže slosar 1
    Convolutional Autoencoder Sreevarsha Sreejith 1 3 Anže Slosar 1, supplied by Brookhaven Instruments, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/convolution+1/1+1+3+an%C5%BEe+autoencoder+convolutional+slosar+sreejith+sreevarsha/10__1103_slash_physrevd__110__103030-3-6-20
    Average 86 stars, based on 1 article reviews
    convolutional autoencoder sreevarsha sreejith 1 3 anže slosar 1 - by Bioz Stars, 2026-10
    86/100 stars
      Buy from Supplier

    90
    SoftMax Inc 1-d convolution
    1 D Convolution, 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/product/convolution+1/convolution+1d/pm38918526-112-186-192
    Average 90 stars, based on 1 article reviews
    1-d convolution - by Bioz Stars, 2026-10
    90/100 stars
      Buy from Supplier

    90
    SoftMax Inc softmax-activated 1 × 1 convolution
    Softmax Activated 1 × 1 Convolution, 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/product/convolution+1/softmax+activated+1+%C3%97+1+convolution/pm38002391-211-6-2
    Average 90 stars, based on 1 article reviews
    softmax-activated 1 × 1 convolution - by Bioz Stars, 2026-10
    90/100 stars
      Buy from Supplier

    90
    SoftMax Inc 1 × 1 convolutional with the softmax function
    1 × 1 Convolutional With The Softmax Function, 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/product/convolution+1/1+%C3%97+1+convolutional+with+the+softmax+function/pm37175049-199-17-17
    Average 90 stars, based on 1 article reviews
    1 × 1 convolutional with the softmax function - by Bioz Stars, 2026-10
    90/100 stars
      Buy from Supplier

    90
    SoftMax Inc convolution 1
    Convolution 1, 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/product/convolution+1/convolution+1d/pm36613360-250-19-54
    Average 90 stars, based on 1 article reviews
    convolution 1 - by Bioz Stars, 2026-10
    90/100 stars
      Buy from Supplier

    90
    Photonics Inc 1 × 1 kernel convolution
    1 × 1 Kernel Convolution, supplied by Photonics 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/product/convolution+1/1+%C3%97+1+kernel+convolution/10__3390_slash_photonics9030150-152-26-16
    Average 90 stars, based on 1 article reviews
    1 × 1 kernel convolution - by Bioz Stars, 2026-10
    90/100 stars
      Buy from Supplier

    90
    SoftMax Inc 1 × 1 convolution with softmax activation
    Illustration of SegNet architecture for calcium segmentation. The encoder is composed of a 3 × 3 <t>convolution,</t> batch normalization, and rectified linear unit layers. The decoder upsamples the low-resolution feature map using the transferred pooling indices from the counterpart encoder. The final output of decoder is fed to the Softmax activation to produce a pixel-wise classification map. The input is the preprocessed image selected by the classification model (step 1), and the output is predicted label. The sizes of input and output images are the same (200 × 448 pixels). In the input image, the black strip indicates the removed guidewire shadow.
    1 × 1 Convolution With Softmax Activation, 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/product/convolution+1/conv+3%C3%973+mbconvl++3%C3%973+mbconv6++3%C3%973+mbconv6++5%C3%975+conv+1%C3%971+pooling+fc/pmc07885992-122-16-18
    Average 90 stars, based on 1 article reviews
    1 × 1 convolution with softmax activation - by Bioz Stars, 2026-10
    90/100 stars
      Buy from Supplier

    90
    SoftMax Inc 1 × 1 convolution with the softmax activation function
    Illustration of SegNet architecture for calcium segmentation. The encoder is composed of a 3 × 3 <t>convolution,</t> batch normalization, and rectified linear unit layers. The decoder upsamples the low-resolution feature map using the transferred pooling indices from the counterpart encoder. The final output of decoder is fed to the Softmax activation to produce a pixel-wise classification map. The input is the preprocessed image selected by the classification model (step 1), and the output is predicted label. The sizes of input and output images are the same (200 × 448 pixels). In the input image, the black strip indicates the removed guidewire shadow.
    1 × 1 Convolution With The Softmax Activation Function, 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/product/convolution+1/conv+3%C3%973+mbconvl++3%C3%973+mbconv6++3%C3%973+mbconv6++5%C3%975+conv+1%C3%971+pooling+fc/pmc07436155-137-23-26
    Average 90 stars, based on 1 article reviews
    1 × 1 convolution with the softmax activation function - by Bioz Stars, 2026-10
    90/100 stars
      Buy from Supplier

    90
    MathWorks Inc alexnet layer 1 convolution operation
    Illustration of SegNet architecture for calcium segmentation. The encoder is composed of a 3 × 3 <t>convolution,</t> batch normalization, and rectified linear unit layers. The decoder upsamples the low-resolution feature map using the transferred pooling indices from the counterpart encoder. The final output of decoder is fed to the Softmax activation to produce a pixel-wise classification map. The input is the preprocessed image selected by the classification model (step 1), and the output is predicted label. The sizes of input and output images are the same (200 × 448 pixels). In the input image, the black strip indicates the removed guidewire shadow.
    Alexnet Layer 1 Convolution Operation, supplied by MathWorks 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/product/convolution+1/10__3390_slash_electronics9030414-54-1-9
    Average 90 stars, based on 1 article reviews
    alexnet layer 1 convolution operation - by Bioz Stars, 2026-10
    90/100 stars
      Buy from Supplier

    90
    SoftMax Inc convolution (1 × 1)
    The proposed CNN’s layers
    Convolution (1 × 1), 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/product/convolution+1/convolution+1d/pmc06839032-33-2-0
    Average 90 stars, based on 1 article reviews
    convolution (1 × 1) - by Bioz Stars, 2026-10
    90/100 stars
      Buy from Supplier

    Image Search Results


    Illustration of SegNet architecture for calcium segmentation. The encoder is composed of a 3 × 3 convolution, batch normalization, and rectified linear unit layers. The decoder upsamples the low-resolution feature map using the transferred pooling indices from the counterpart encoder. The final output of decoder is fed to the Softmax activation to produce a pixel-wise classification map. The input is the preprocessed image selected by the classification model (step 1), and the output is predicted label. The sizes of input and output images are the same (200 × 448 pixels). In the input image, the black strip indicates the removed guidewire shadow.

    Journal: IEEE access : practical innovations, open solutions

    Article Title: Segmentation of Coronary Calcified Plaque in Intravascular OCT Images Using a Two-Step Deep Learning Approach

    doi: 10.1109/access.2020.3045285

    Figure Lengend Snippet: Illustration of SegNet architecture for calcium segmentation. The encoder is composed of a 3 × 3 convolution, batch normalization, and rectified linear unit layers. The decoder upsamples the low-resolution feature map using the transferred pooling indices from the counterpart encoder. The final output of decoder is fed to the Softmax activation to produce a pixel-wise classification map. The input is the preprocessed image selected by the classification model (step 1), and the output is predicted label. The sizes of input and output images are the same (200 × 448 pixels). In the input image, the black strip indicates the removed guidewire shadow.

    Article Snippet: The restored feature map was fed to the final classification layer including a 1 × 1 convolution with Softmax activation to produce class probabilities for each pixel.

    Techniques: Activation Assay, Stripping Membranes

    The proposed CNN’s layers

    Journal: GigaScience

    Article Title: RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures

    doi: 10.1093/gigascience/giz123

    Figure Lengend Snippet: The proposed CNN’s layers

    Article Snippet: Softmax , Convolution (1 × 1) , 512 × 512 , 3.

    Techniques: Blocking Assay