Noise Injection
Noise injection is a regularization technique that adds random noise to inputs, weights, or gradients during training. It improves model robustness and generalization by preventing over-reliance on specific patterns.
Understanding Noise Injection
Noise injection is a regularization technique where random perturbations are deliberately added to the inputs, weights, or activations of a neural network during training to improve generalization and robustness. By exposing the model to slightly corrupted versions of the data, noise injection prevents overfitting to specific training examples and encourages learning more stable, broadly applicable features. Dropout, one of the most widely used regularization methods, can be viewed as a form of noise injection applied to neuron activations. In generative models, noise plays a central role in diffusion-based architectures that power modern text-to-image systems. Noise injection is also used as a data augmentation strategy in computer vision and speech processing, and it is foundational to the training of masked autoencoders. Careful calibration of noise levels is important, as excessive noise can degrade learning while too little provides insufficient regularization.
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.