Depthwise Separable Convolution
Depthwise separable convolution is an efficient convolution variant that factorizes a standard convolution into depthwise and pointwise operations. It dramatically reduces computation while maintaining accuracy, enabling mobile AI.
Understanding Depthwise Separable Convolution
Depthwise separable convolution is a computationally efficient alternative to standard convolution operations in convolutional neural networks. It factorizes a standard convolution into two separate steps: a depthwise convolution that applies a single filter per input channel, followed by a pointwise convolution that combines the outputs across channels. This decomposition dramatically reduces the number of parameters and multiply-accumulate operations, making models faster and more memory-efficient without significantly sacrificing accuracy. MobileNet and EfficientNet architectures rely heavily on depthwise separable convolutions to achieve strong performance on mobile and edge devices. This technique is particularly important for real-time applications like object tracking, pose estimation, and instance segmentation on AI chips with limited computational budgets, enabling deployment of powerful computer vision models on smartphones and embedded systems.
Category
Deep Learning
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Activation Function
An activation function is a mathematical function applied to a neuron's output to introduce non-linearity into a neural network. Common activation functions include ReLU, sigmoid, and tanh, each with different properties for gradient flow.
Adam Optimizer
Adam (Adaptive Moment Estimation) is an optimization algorithm that combines the benefits of AdaGrad and RMSProp. It adapts learning rates for each parameter using estimates of first and second moments of gradients.
Adapter Layers
Adapter layers are small trainable modules inserted into a pre-trained model to enable parameter-efficient fine-tuning. They allow task adaptation while keeping the original model weights frozen.
Attention Mechanism
An attention mechanism allows neural networks to focus on the most relevant parts of the input when producing each element of the output. Attention is the foundational innovation behind the Transformer architecture and modern large language models.
Autoencoder
An autoencoder is a neural network trained to compress input data into a compact representation and then reconstruct it. Autoencoders are used for dimensionality reduction, denoising, and learning latent representations.
Backpropagation
Backpropagation is the algorithm used to train neural networks by computing gradients of the loss function with respect to each weight. It propagates error signals backward through the network to update weights and minimize prediction errors.
Batch Normalization
Batch normalization is a technique that normalizes layer inputs across mini-batches during training to stabilize and accelerate neural network training. It reduces internal covariate shift and allows higher learning rates.
Batch Size
Batch size is the number of training examples used in one iteration of gradient descent. Larger batches provide more stable gradient estimates but require more memory, while smaller batches add beneficial noise.