Catastrophic Forgetting
Catastrophic forgetting is the tendency of neural networks to abruptly lose previously learned knowledge when trained on new tasks. Continual learning research aims to overcome this limitation.
Understanding Catastrophic Forgetting
Catastrophic forgetting occurs when a neural network, upon learning new tasks or data, rapidly loses the knowledge it acquired during previous training phases. This phenomenon is a fundamental challenge in machine learning, particularly when models need to be updated incrementally rather than retrained from scratch. For example, a language model fine-tuned on medical text might lose its general conversational abilities if not handled carefully. The problem is especially relevant to continual learning scenarios where models must adapt to shifting data distributions over time. Researchers address catastrophic forgetting through techniques like elastic weight consolidation, adapter layers, and replay-based methods that periodically revisit earlier training examples. Understanding and mitigating this issue is essential for building robust AI systems that can evolve without sacrificing previously learned capabilities.
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.