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Learning to Select Pre-Trained Deep Representations with Bayesian Evidence Framework

https://doi.org/10.1109/cvpr.2016.574

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-319-10590-1_53openalex-snapshot

Visualizing and Understanding Convolutional Networks

inferredcites →https://doi.org/10.1007/978-3-319-10590-1_54openalex-snapshot

Part-Based R-CNNs for Fine-Grained Category Detection

inferredcites →https://doi.org/10.1007/s11263-014-0733-5openalex-snapshot

The Pascal Visual Object Classes Challenge: A Retrospective

inferredcites →https://doi.org/10.1016/j.cviu.2005.09.012openalex-snapshot

Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories

inferredcites →https://doi.org/10.1017/s0370164600022070openalex-snapshot

XXV.—On Bernoulli's Numerical Solution of Algebraic Equations

inferredcites →https://doi.org/10.1023/a:1018628609742openalex-snapshot

Least Squares Support Vector Machine Classifiers

inferredcites →https://doi.org/10.1023/b:mach.0000008082.80494.e0openalex-snapshot

Benchmarking Least Squares Support Vector Machine Classifiers

inferredcites →https://doi.org/10.1093/oso/9780198538493.001.0001openalex-snapshot

Neural Networks for Pattern Recognition

inferredcites →https://doi.org/10.1109/cvpr.2009.5206537openalex-snapshot

Recognizing indoor scenes

inferredcites →https://doi.org/10.1109/cvpr.2009.5206772openalex-snapshot

Describing objects by their attributes

inferredcites →https://doi.org/10.1109/cvpr.2010.5539970openalex-snapshot

SUN database: Large-scale scene recognition from abbey to zoo

inferredcites →https://doi.org/10.1109/cvpr.2014.212openalex-snapshot

PANDA: Pose Aligned Networks for Deep Attribute Modeling

inferredcites →https://doi.org/10.1109/cvpr.2014.222openalex-snapshot

Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks

inferredcites →https://doi.org/10.1109/cvpr.2014.81openalex-snapshot

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

inferredcites →https://doi.org/10.1109/cvpr.2015.7298594openalex-snapshot

Going deeper with convolutions

inferredcites →https://doi.org/10.1109/cvpr.2016.465openalex-snapshot

Learning Multi-domain Convolutional Neural Networks for Visual Tracking

inferredcites →https://doi.org/10.1109/cvprw.2014.131openalex-snapshot

CNN Features Off-the-Shelf: An Astounding Baseline for Recognition

inferredcites →https://doi.org/10.1109/cvprw.2015.7301270openalex-snapshot

From generic to specific deep representations for visual recognition

inferredcites →https://doi.org/10.1109/iccv.2011.6126386openalex-snapshot

Human action recognition by learning bases of action attributes and parts

inferredcites →https://doi.org/10.1109/iccv.2011.6126413openalex-snapshot

Describing people: A poselet-based approach to attribute classification

inferredcites →https://doi.org/10.1109/iccv.2015.178openalex-snapshot

Learning Deconvolution Network for Semantic Segmentation

inferredcites →https://doi.org/10.1109/icpr.2004.1334050openalex-snapshot

SVM vs regularized least squares classification

inferredcites →https://doi.org/10.1109/icvgip.2008.47openalex-snapshot

Automated Flower Classification over a Large Number of Classes

inferredcites →https://doi.org/10.1162/089976602753633411openalex-snapshot

Bayesian Framework for Least-Squares Support Vector Machine Classifiers, Gaussian Processes, and Kernel Fisher Discriminant Analysis

inferredcites →https://doi.org/10.1162/neco.1992.4.3.415openalex-snapshot

Bayesian Interpolation

inferredcites →https://doi.org/10.48550/arxiv.1310.1531openalex-snapshot

DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot

Very Deep Convolutional Networks for Large-Scale Image Recognition

inferredcites →https://doi.org/10.5244/c.28.6openalex-snapshot

Return of the Devil in the Details: Delving Deep into Convolutional Nets

inferredcites →https://doi.org/10.57702/i9oeewf6openalex-snapshot

Caltech-UCSD Birds-200-2011 Dataset

inferredcites →https://doi.org/10.5860/choice.31-5500openalex-snapshot

Neural networks for pattern recognition

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Selecting Algorithms Without Meta-features

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Real-world plant species identification based on deep convolutional neural networks and visual attention

inferred← citeshttps://doi.org/10.1016/j.neunet.2017.02.008openalex-snapshot

Dual-memory neural networks for modeling cognitive activities of humans via wearable sensors

inferred← citeshttps://doi.org/10.1109/cvpr.2017.9openalex-snapshot

Borrowing Treasures from the Wealthy: Deep Transfer Learning through Selective Joint Fine-Tuning

inferred← citeshttps://doi.org/10.1109/cvpr.2019.00315openalex-snapshot

Weakly Supervised Complementary Parts Models for Fine-Grained Image Classification From the Bottom Up

inferred← citeshttps://doi.org/10.1109/dt.2016.7557169openalex-snapshot

High-level verification of multi-object segmentation

inferred← citeshttps://doi.org/10.1109/tip.2019.2908795openalex-snapshot

Foreground Fisher Vector: Encoding Class-Relevant Foreground to Improve Image Classification

inferred← citeshttps://doi.org/10.1109/tmm.2017.2766842openalex-snapshot

Bag of Surrogate Parts Feature for Visual Recognition

inferred← citeshttps://doi.org/10.1109/tmm.2021.3121547openalex-snapshot

Combining Deep Convolutional Neural Networks With Stochastic Ensemble Weight Optimization for Facial Expression Recognition in the Wild

inferred← citeshttps://doi.org/10.1109/wacv.2019.00039openalex-snapshot

Good Choices for Deep Convolutional Feature Encoding

inferred← citeshttps://doi.org/10.1145/3774904.3792172openalex-snapshot

Graph Neural Network Model Transferability Estimation via Decomposition-Augmented Discriminant Analysis

inferred← citeshttps://doi.org/10.5624/isd.2019.49.1.1openalex-snapshot

An overview of deep learning in the field of dentistry

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