7.2 Feature extraction and hierarchical representations in CNNs
2 min read•july 25, 2024
Convolutional Neural Networks (CNNs) mimic human visual processing by building progressively complex representations. From detecting basic visual elements to assembling , CNNs use hierarchical feature representations to process images effectively.
CNNs employ convolutional layers, pooling layers, and activation functions to learn and extract features. This hierarchical approach allows for the detection of local patterns through receptive fields and the development of increasingly abstract representations in deeper layers.
Hierarchical Representations in CNNs
Hierarchical feature representations in CNNs
Top images from around the web for Hierarchical feature representations in CNNs
Introspective analysis of convolutional neural networks for improving discrimination performance ... View original
Is this image relevant?
Convolutional neural network - Wikipedia View original
Is this image relevant?
Hands-on: Deep Learning (Part 3) - Convolutional neural networks (CNN) / Deep Learning (Part 3 ... View original
Is this image relevant?
Introspective analysis of convolutional neural networks for improving discrimination performance ... View original
Is this image relevant?
Convolutional neural network - Wikipedia View original
Is this image relevant?
1 of 3
Top images from around the web for Hierarchical feature representations in CNNs
Introspective analysis of convolutional neural networks for improving discrimination performance ... View original
Is this image relevant?
Convolutional neural network - Wikipedia View original
Is this image relevant?
Hands-on: Deep Learning (Part 3) - Convolutional neural networks (CNN) / Deep Learning (Part 3 ... View original
Is this image relevant?
Introspective analysis of convolutional neural networks for improving discrimination performance ... View original
Is this image relevant?
Convolutional neural network - Wikipedia View original
Is this image relevant?
1 of 3
Feature hierarchy in CNNs mimics human visual processing builds progressively complex representations
Low-level features (early layers) detect basic visual elements (, , simple )
Mid-level features (intermediate layers) combine low-level features form and
High-level features (deeper layers) assemble complex object structures and scene compositions
Convolutional layers apply learnable filters detect specific patterns each layer builds upon previous layer's features
Pooling layers reduce spatial dimensions increase invariance to small translations (, )
Activation functions (ReLU, sigmoid) introduce enable learning of complex patterns
Receptive fields for local patterns
refers to region in input space affecting particular CNN feature grows larger in deeper layers
limits each neuron's connections to small region of previous layer preserves spatial relationships
Receptive field size increases in deeper layers influenced by , , and pooling operations
Enables detection of local features at various scales (textures, object parts)
Overlapping receptive fields allow feature detection at different locations
Field of view expands with network depth captures larger context for global understanding
Deeper layers for complex features
Increasing abstraction shallow layers detect simple features (edges, colors) deep layers capture complex composite features (faces, vehicles)
Feature composition deeper layers combine lower-level features create more abstract representations
larger receptive fields in deeper layers capture relationships between distant parts of input (scene layout)
deeper layers become more robust to input transformations (rotation, scale)
aid understanding (, activation maximization)
deeper layers more task-specific earlier layers more general and transferable
Feature extraction importance in vision
Automatic feature learning CNNs learn relevant features without manual engineering adapt to various tasks and datasets
tailored for different vision tasks (classification, detection, segmentation, recognition)
pre-trained models serve as feature extractors fine-tuned for specific tasks
to variations handles changes in illumination, pose, and occlusions
creates compact representations of high-dimensional image data
Interpretability analysis of learned features improves model understanding
Performance improvements increases accuracy in vision tasks enables efficient processing of large-scale datasets