Contrastive Learning
Contrastive learning is a self-supervised technique that trains models to distinguish between similar and dissimilar data pairs. It learns useful representations by pulling similar examples closer and pushing dissimilar ones apart in embedding space.
Understanding Contrastive Learning
Contrastive learning is a self-supervised technique that teaches neural networks to produce meaningful representations by learning which data points are similar and which are different, without requiring labeled training data. The core principle involves creating positive pairs (augmented versions of the same data point) that should be close in the embedding space, and negative pairs (different data points) that should be far apart. Frameworks like SimCLR and MoCo demonstrated that contrastive learning on unlabeled images can rival supervised training on ImageNet. The technique extends beyond computer vision to natural language processing and multimodal learning, with CLIP using contrastive learning to align image and text representations. Unlike autoencoder-based approaches that focus on reconstruction, contrastive learning optimizes relative similarity and often produces more transferable features. Its ability to leverage vast amounts of unlabeled data makes it valuable when annotation is expensive, reducing the need for active learning.
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.