applicable models
neural network
Techniques for general neural network architectures
15 techniques
| Goals | Models | Data Types | Description | |||
|---|---|---|---|---|---|---|
| Integrated Gradients | Algorithmic | Neural Network | Any | Integrated Gradients is an attribution technique that explains a model's prediction by quantifying the contribution of... | ||
| DeepLIFT | Algorithmic | Neural Network | Any | DeepLIFT (Deep Learning Important FeaTures) explains neural network predictions by decomposing the difference between... | ||
| Layer-wise Relevance Propagation | Algorithmic | Neural Network | Any | Layer-wise Relevance Propagation (LRP) explains neural network predictions by working backwards through the network to... | ||
| Taylor Decomposition | Algorithmic | Neural Network CNN | Any | Taylor Decomposition is a mathematical technique that explains neural network predictions by computing first-order and... | ||
| Saliency Maps | Algorithmic | Neural Network | Image | Saliency maps are visual explanations for image classification models that highlight which pixels in an image most... | ||
| Monte Carlo Dropout | Algorithmic | Neural Network | Any | Monte Carlo Dropout estimates prediction uncertainty by applying dropout (randomly setting neural network weights to... | ||
| Out-of-DIstribution detector for Neural networks | Algorithmic | Neural Network | Any | ODIN (Out-of-Distribution Detector for Neural Networks) identifies when a neural network encounters inputs significantly... | ||
| Adversarial Debiasing | Algorithmic | Neural Network | Any | Adversarial debiasing reduces bias by training models using a competitive adversarial setup, similar to Generative... | ||
| Temperature Scaling | Algorithmic | Neural Network | Any | Temperature scaling adjusts a model's confidence by applying a single parameter (temperature) to its predictions. When a... | ||
| Deep Ensembles | Algorithmic | Neural Network | Any | Deep ensembles combine predictions from multiple neural networks trained independently with different random... | ||
| Model Distillation | Algorithmic | Neural Network | Any | Model distillation transfers knowledge from a large, complex model (teacher) to a smaller, more efficient model... | ||
| Model Pruning | Algorithmic | Neural Network | Any | Model pruning systematically removes less important weights, neurons, or entire layers from neural networks to create... | ||
| Neuron Activation Analysis | Algorithmic | Neural Network LLM +1 | Text | Neuron activation analysis examines the firing patterns of individual neurons in neural networks by probing them with... | ||
| Concept Activation Vectors | Algorithmic | Neural Network Transformer +1 | Any | Concept Activation Vectors (CAVs), also known as Testing with Concept Activation Vectors (TCAV), identify mathematical... | ||
| Fair Adversarial Networks | Algorithmic | Neural Network CNN +1 | Any | An in-processing fairness technique that employs adversarial training with dual neural networks to learn fair... |
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