Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review

Citation edge evidence

Each row is directed from the citing work to the cited work. Source status remains readable without the interactive graph.

• A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures→Deep Convolutional Neural Networks for Image Classification: A Comprehensive ReviewLegacy edge — source detail unavailable
• A State-of-the-Art Survey on Deep Learning Theory and Architectures→Deep Convolutional Neural Networks for Image Classification: A Comprehensive ReviewLegacy edge — source detail unavailable
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review→Long Short-Term MemoryLegacy edge — source detail unavailable
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review→Adam: A Method for Stochastic OptimizationLegacy edge — source detail unavailable
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review→Dropout: a simple way to prevent neural networks from overfittingLegacy edge — source detail unavailable
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review→Learning representations by back-propagating errorsLegacy edge — source detail unavailable
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review→Reducing the Dimensionality of Data with Neural NetworksLegacy edge — source detail unavailable
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review→Xception: Deep Learning with Depthwise Separable ConvolutionsLegacy edge — source detail unavailable
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review→Deep learning in neural networks: An overviewLegacy edge — source detail unavailable
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review→A Fast Learning Algorithm for Deep Belief NetsLegacy edge — source detail unavailable
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review→Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Sh…Legacy edge — source detail unavailable
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review→Advances in Neural Information Processing Systems 19Legacy edge — source detail unavailable
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review→A new optimizer using particle swarm theoryLegacy edge — source detail unavailable
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review→Advances in Neural Information Processing Systems 28Legacy edge — source detail unavailable
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review→Distilling the Knowledge in a Neural NetworkLegacy edge — source detail unavailable
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review→Inception-v4, Inception-ResNet and the Impact of Residual Connections on LearningLegacy edge — source detail unavailable
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review→Understanding the difficulty of training deep feedforward neural networksLegacy edge — source detail unavailable
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review→Aggregated Residual Transformations for Deep Neural NetworksLegacy edge — source detail unavailable
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review→Adaptive Subgradient Methods for Online Learning and Stochastic OptimizationLegacy edge — source detail unavailable
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review→Learning long-term dependencies with gradient descent is difficultLegacy edge — source detail unavailable
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review→Domain-Adversarial Training of Neural NetworksLegacy edge — source detail unavailable
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review→Learning Deep Architectures for AILegacy edge — source detail unavailable
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review→Improving neural networks by preventing co-adaptation of feature detectorsLegacy edge — source detail unavailable
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review→Proceedings of the 19th International Joint Conference on Artificial IntelligenceLegacy edge — source detail unavailable
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review→Large-Scale Machine Learning with Stochastic Gradient DescentLegacy edge — source detail unavailable
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review→ADADELTA: An Adaptive Learning Rate MethodLegacy edge — source detail unavailable
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review→Deep Sparse Rectifier Neural NetworksLegacy edge — source detail unavailable
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review→Learning Deep Architectures for AILegacy edge — source detail unavailable
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review→Training Products of Experts by Minimizing Contrastive DivergenceLegacy edge — source detail unavailable
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review→On the importance of initialization and momentum in deep learningLegacy edge — source detail unavailable
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review→Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Ex…Legacy edge — source detail unavailable
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review→One-shot learning of object categoriesLegacy edge — source detail unavailable
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review→Convergence Results for Neural Networks via ElectrodynamicsLegacy edge — source detail unavailable
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review→Advances in Neural Information Processing Systems 37Legacy edge — source detail unavailable
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review→Proceedings of the International Joint Conference on Artificial Intelligence 2007Legacy edge — source detail unavailable
• On the importance of initialization and momentum in deep learning→Understanding the difficulty of training deep feedforward neural networksLegacy edge — source detail unavailable
• On the importance of initialization and momentum in deep learning→Long Short-Term MemoryLegacy edge — source detail unavailable
• On the importance of initialization and momentum in deep learning→Reducing the Dimensionality of Data with Neural NetworksLegacy edge — source detail unavailable
• On the importance of initialization and momentum in deep learning→Learning long-term dependencies with gradient descent is difficultLegacy edge — source detail unavailable
• On the importance of initialization and momentum in deep learning→A Fast Learning Algorithm for Deep Belief NetsLegacy edge — source detail unavailable
• Large-Scale Machine Learning with Stochastic Gradient Descent→Advances in Neural Information Processing Systems 19Legacy edge — source detail unavailable
• Adam: A Method for Stochastic Optimization→On the importance of initialization and momentum in deep learningLegacy edge — source detail unavailable
• Adam: A Method for Stochastic Optimization→Improving neural networks by preventing co-adaptation of feature detectorsLegacy edge — source detail unavailable
• Adam: A Method for Stochastic Optimization→Reducing the Dimensionality of Data with Neural NetworksLegacy edge — source detail unavailable
• Adam: A Method for Stochastic Optimization→Adaptive Subgradient Methods for Online Learning and Stochastic OptimizationLegacy edge — source detail unavailable
• Adam: A Method for Stochastic Optimization→ADADELTA: An Adaptive Learning Rate MethodLegacy edge — source detail unavailable
• Understanding the difficulty of training deep feedforward neural networks→Learning representations by back-propagating errorsLegacy edge — source detail unavailable
• Understanding the difficulty of training deep feedforward neural networks→Learning Deep Architectures for AILegacy edge — source detail unavailable
• Understanding the difficulty of training deep feedforward neural networks→Learning long-term dependencies with gradient descent is difficultLegacy edge — source detail unavailable
• Understanding the difficulty of training deep feedforward neural networks→A Fast Learning Algorithm for Deep Belief NetsLegacy edge — source detail unavailable
• Domain-Adversarial Training of Neural Networks→Dropout: a simple way to prevent neural networks from overfittingLegacy edge — source detail unavailable
• Domain-Adversarial Training of Neural Networks→ADADELTA: An Adaptive Learning Rate MethodLegacy edge — source detail unavailable
• Distilling the Knowledge in a Neural Network→Improving neural networks by preventing co-adaptation of feature detectorsLegacy edge — source detail unavailable
• Distilling the Knowledge in a Neural Network→Dropout: a simple way to prevent neural networks from overfittingLegacy edge — source detail unavailable
• Improving neural networks by preventing co-adaptation of feature detectors→Learning representations by back-propagating errorsLegacy edge — source detail unavailable
• Improving neural networks by preventing co-adaptation of feature detectors→Reducing the Dimensionality of Data with Neural NetworksLegacy edge — source detail unavailable
• Improving neural networks by preventing co-adaptation of feature detectors→Training Products of Experts by Minimizing Contrastive DivergenceLegacy edge — source detail unavailable
• Long Short-Term Memory→Learning long-term dependencies with gradient descent is difficultLegacy edge — source detail unavailable
• Learning Deep Architectures for AI→Learning representations by back-propagating errorsLegacy edge — source detail unavailable
• Learning Deep Architectures for AI→Reducing the Dimensionality of Data with Neural NetworksLegacy edge — source detail unavailable
• Learning Deep Architectures for AI→Learning long-term dependencies with gradient descent is difficultLegacy edge — source detail unavailable
• Learning Deep Architectures for AI→Training Products of Experts by Minimizing Contrastive DivergenceLegacy edge — source detail unavailable
• Learning Deep Architectures for AI→A Fast Learning Algorithm for Deep Belief NetsLegacy edge — source detail unavailable
• Learning Deep Architectures for AI→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Deep learning in neural networks: An overview→Improving neural networks by preventing co-adaptation of feature detectorsLegacy edge — source detail unavailable
• Deep learning in neural networks: An overview→Long Short-Term MemoryLegacy edge — source detail unavailable
• Deep learning in neural networks: An overview→Learning Deep Architectures for AILegacy edge — source detail unavailable
• Deep learning in neural networks: An overview→Reducing the Dimensionality of Data with Neural NetworksLegacy edge — source detail unavailable
• Deep learning in neural networks: An overview→Learning long-term dependencies with gradient descent is difficultLegacy edge — source detail unavailable
• Deep learning in neural networks: An overview→Training Products of Experts by Minimizing Contrastive DivergenceLegacy edge — source detail unavailable
• Deep learning in neural networks: An overview→A Fast Learning Algorithm for Deep Belief NetsLegacy edge — source detail unavailable
• Deep learning in neural networks: An overview→Adaptive Subgradient Methods for Online Learning and Stochastic OptimizationLegacy edge — source detail unavailable
• Deep learning in neural networks: An overview→Deep Sparse Rectifier Neural NetworksLegacy edge — source detail unavailable
• Deep learning in neural networks: An overview→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Deep learning in neural networks: An overview→Learning Deep Architectures for AILegacy edge — source detail unavailable
• Deep learning in neural networks: An overview→ADADELTA: An Adaptive Learning Rate MethodLegacy edge — source detail unavailable
• Dropout: a simple way to prevent neural networks from overfitting→Reducing the Dimensionality of Data with Neural NetworksLegacy edge — source detail unavailable
• Dropout: a simple way to prevent neural networks from overfitting→A Fast Learning Algorithm for Deep Belief NetsLegacy edge — source detail unavailable
• Reducing the Dimensionality of Data with Neural Networks→A Fast Learning Algorithm for Deep Belief NetsLegacy edge — source detail unavailable
• Learning long-term dependencies with gradient descent is difficult→Advances in Neural Information Processing Systems 37Legacy edge — source detail unavailable
• A Fast Learning Algorithm for Deep Belief Nets→Training Products of Experts by Minimizing Contrastive DivergenceLegacy edge — source detail unavailable
• Deep Sparse Rectifier Neural Networks→Understanding the difficulty of training deep feedforward neural networksLegacy edge — source detail unavailable
• Deep Sparse Rectifier Neural Networks→Learning Deep Architectures for AILegacy edge — source detail unavailable
• Deep Sparse Rectifier Neural Networks→A Fast Learning Algorithm for Deep Belief NetsLegacy edge — source detail unavailable
• Xception: Deep Learning with Depthwise Separable Convolutions→Distilling the Knowledge in a Neural NetworkLegacy edge — source detail unavailable
• Xception: Deep Learning with Depthwise Separable Convolutions→Convergence Results for Neural Networks via ElectrodynamicsLegacy edge — source detail unavailable
• Xception: Deep Learning with Depthwise Separable Convolutions→Inception-v4, Inception-ResNet and the Impact of Residual Connections on LearningLegacy edge — source detail unavailable
• Aggregated Residual Transformations for Deep Neural Networks→Inception-v4, Inception-ResNet and the Impact of Residual Connections on LearningLegacy edge — source detail unavailable
• A State-of-the-Art Survey on Deep Learning Theory and Architectures→On the importance of initialization and momentum in deep learningLegacy edge — source detail unavailable
• A State-of-the-Art Survey on Deep Learning Theory and Architectures→Learning representations by back-propagating errorsLegacy edge — source detail unavailable
• A State-of-the-Art Survey on Deep Learning Theory and Architectures→Understanding the difficulty of training deep feedforward neural networksLegacy edge — source detail unavailable
• A State-of-the-Art Survey on Deep Learning Theory and Architectures→Domain-Adversarial Training of Neural NetworksLegacy edge — source detail unavailable
• A State-of-the-Art Survey on Deep Learning Theory and Architectures→Deep learning in neural networks: An overviewLegacy edge — source detail unavailable
• A State-of-the-Art Survey on Deep Learning Theory and Architectures→Dropout: a simple way to prevent neural networks from overfittingLegacy edge — source detail unavailable
• A State-of-the-Art Survey on Deep Learning Theory and Architectures→Reducing the Dimensionality of Data with Neural NetworksLegacy edge — source detail unavailable
• A State-of-the-Art Survey on Deep Learning Theory and Architectures→A Fast Learning Algorithm for Deep Belief NetsLegacy edge — source detail unavailable
• A State-of-the-Art Survey on Deep Learning Theory and Architectures→Xception: Deep Learning with Depthwise Separable ConvolutionsLegacy edge — source detail unavailable
• A State-of-the-Art Survey on Deep Learning Theory and Architectures→Aggregated Residual Transformations for Deep Neural NetworksLegacy edge — source detail unavailable
• A State-of-the-Art Survey on Deep Learning Theory and Architectures→Inception-v4, Inception-ResNet and the Impact of Residual Connections on LearningLegacy edge — source detail unavailable
• A State-of-the-Art Survey on Deep Learning Theory and Architectures→Learning Deep Architectures for AILegacy edge — source detail unavailable
• A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures→Long Short-Term MemoryLegacy edge — source detail unavailable
• A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures→Learning Deep Architectures for AILegacy edge — source detail unavailable
• A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures→Learning long-term dependencies with gradient descent is difficultLegacy edge — source detail unavailable
• A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures→Proceedings of the 19th International Joint Conference on Artificial IntelligenceLegacy edge — source detail unavailable
• A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures→Advances in Neural Information Processing Systems 28Legacy edge — source detail unavailable
• A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures→Proceedings of the International Joint Conference on Artificial Intelligence 2007Legacy edge — source detail unavailable
• A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures→Learning Deep Architectures for AILegacy edge — source detail unavailable
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Sh…→On the importance of initialization and momentum in deep learningLegacy edge — source detail unavailable
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Sh…→Dropout: a simple way to prevent neural networks from overfittingLegacy edge — source detail unavailable
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Sh…→Adaptive Subgradient Methods for Online Learning and Stochastic OptimizationLegacy edge — source detail unavailable
• Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning→On the importance of initialization and momentum in deep learningLegacy edge — source detail unavailable
• ADADELTA: An Adaptive Learning Rate Method→Learning representations by back-propagating errorsLegacy edge — source detail unavailable