Deep Learning-Based Approaches
Deep learning, a subset of machine learning, leverages neural networks with multiple layers (hence “deep”) to automatically learn features and perform tasks end-to-end. Convolutional Neural Networks (CNNs) are the cornerstone of deep learning-based computer vision.
Key Components of Deep Learning-Based Approaches
- Convolutional Layers:
- Filters: Convolutional layers apply filters to the input image to create feature maps, automatically learning spatial hierarchies of features.
- Pooling Layers:
- Downsampling: Pooling layers (e.g., max pooling, average pooling) reduce the spatial dimensions of the feature maps, retaining essential features while reducing computational load.
- Fully Connected Layers:
- Classification: After several convolutional and pooling layers, the feature maps are flattened and passed through fully connected layers to perform classification or regression tasks.
- Activation Functions:
- Non-linearity: Functions like ReLU (Rectified Linear Unit), sigmoid, and tanh introduce non-linearity into the network, enabling it to learn complex patterns.
- Training and Optimization:
- Backpropagation: The network is trained using backpropagation and gradient descent to minimize the loss function, adjusting weights and biases to improve performance.
Difference between Traditional Computer Vision Techniques and Deep Learning-based Approaches
Computer vision enables machines to interpret and understand the visual world. Over the years, two main approaches have dominated the field: traditional computer vision techniques and deep learning-based approaches.
This article delves into the fundamental differences between these two methodologies and how can be answered in the interview.