Frozen Layers
Frozen layers are neural network layers whose weights are not updated during fine-tuning. Freezing preserves learned representations from pre-training while allowing later layers to adapt to new tasks.
Understanding Frozen Layers
Frozen layers are layers in a neural network whose weights are held constant and not updated during training, typically to preserve knowledge learned during pre-training while adapting other parts of the model to a new task. This technique is fundamental to transfer learning and fine-tuning workflows, where early layers capturing general features like edges in images or syntactic patterns in text remain fixed while later layers are retrained for task-specific predictions. Freezing layers reduces computational cost and memory requirements, making it practical to adapt large foundation models on modest hardware. The decision of which layers to freeze is a key hyperparameter: freezing too many layers can limit the model's ability to adapt, while freezing too few may overwrite useful pre-trained representations, especially with small datasets. Frozen layers are commonly used alongside distillation and parameter-efficient methods for deploying models in edge AI scenarios.
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.