The slide depicts the core CNN architecture workflow for image classification, from raw RGB input tensor through convolutional layers for feature extraction (e.g., edges, textures) and pooling layers for downsampling. It continues to fully connected layers for classification and ends with softmax probability outputs for predictions.
Core Architecture
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Source: CNN Pipeline
Speaker Notes
Good [morning/afternoon], let's dive into the core architecture of Convolutional Neural Networks, or CNNs, which is the backbone of their power in image recognition. As illustrated in this workflow diagram, the process flows linearly from input to output through distinct layers, each serving a critical role.
The journey begins with the **Input Layer**, where a raw image—typically a 2D array of pixel values—is fed into the network. For a color image, this might be a tensor of dimensions height × width × 3 (RGB channels).
Next, **Convolutional Layers** perform feature extraction. Here, small filters or kernels—say 3x3 matrices—slide over the input, computing dot products to produce feature maps. These detect low-level features like edges and gradients in early layers, progressing to complex patterns like shapes in deeper ones. The diagram beside it visualises this: an input image convolved with a kernel yields an activation map, with padding and stride controlling output size.
Then, **Pooling Layers**—often max or average pooling—downsample these feature maps. A 2x2 max pooling window selects the maximum value, halving spatial dimensions while retaining salient features. This introduces translation invariance and reduces computational load, preventing overfitting. The diagram shows a feature map reduced via max pooling.
Following feature processing, **Fully Connected Layers** flatten the pooled maps into a vector and apply dense connections for high-level reasoning and classification. Neurons here integrate global features to output class probabilities via softmax.
Finally, the **Output Layer** delivers predictions, e.g., 'cat' or 'dog' with confidence scores.
This modular design enables hierarchical feature learning, making CNNs efficient for images. We'll explore filters next. Questions? (Approx. 150 words)
