Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition
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- Type
- article
- Published
- 2007-06-17
- Cited by
- 1,186
- References
- 22
- Access
- Open access
- OpenAlex
- https://openalex.org/W2139427956
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:11398758
Keywords
Pattern recognition (psychology), Artificial intelligence, MNIST database, Computer science, Classifier (UML)
References
- Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position
- Object recognition with features inspired by visual cortex
- POP: Patchwork of Parts Models for Object Recognition
- Multiclass Object Recognition with Sparse, Localized Features
- Sparse coding with an overcomplete basis set: a strategy employed by V1?
- Large-scale Learning with SVM and Convolutional for Generic Object Categorization
- Gradient-based learning applied to document recognition
- A Theoretical Analysis of Robust Coding over Noisy Overcomplete Channels
- A Fast Learning Algorithm for Deep Belief Nets
- Distinctive Image Features from Scale-Invariant Keypoints
- Semi-Local Affine Parts for Object Recognition
- Probabilistic visual learning for object detection
- Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
- SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition
- Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories
- Shape matching and object recognition using low distortion correspondences
- Efficient Learning of Sparse Representations with an Energy-Based Model
- Greedy Layer-Wise Training of Deep Networks
- Object Detection and Localization Using Local and Global Features
- GradientBased Learning Applied to Document Recognition
Cited by
- On the Design and Analysis of Multiple View Descriptors
- A hierarchy of recurrent networks for speech recognition
- Random Sampling Image to Class Distance for Photo Annotation
- Sparse coding for machine learning, image processing and computer vision
- Learned Factorization Models to Explain Variability in Natural Image Sequences
- Detecting the pain-evoked P300 in single trials: a comparison of template-based models
- Deep Machine Learning with Spatio-Temporal Inference
- Deep Learning using Robust Interdependent Codes
- Online Multi-Stage Deep Architectures for Feature Extraction and Object Recognition
- Convolutional restricted boltzmann machines for feature learning
- Learning Long-Range Vision for an Offroad Robot
- Training Restricted Boltzmann Machines
- Unsupervised Learning of Visual Representations Using Videos
- Simple low cost causal discovery using mutual information and domain knowledge
- A Physics-Based, Neurobiologically-Inspired Stochastic Framework for Activity Recognition
- Extended Bag-Of-Words Formalism For Image Classification
- Unsupervised Learning of Object Descriptors and Compositions
- Sparse representations for image classification: learning discriminative and reconstructive non-parametric dictionaries
- Handwritten digit recognition using biologically inspired features
- Learning representative and discriminative image representation by deep appearance and spatial coding
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