quark0/darts
Darts
Darts is a differentiable architecture search framework and library designed to automate the discovery of optimal convolutional and recurrent neural network structures. It serves as a research tool for finding high-performing cell topologies using gradient-based optimization.
The framework employs a differentiable cell super-net and weight-sharing mechanisms to identify effective network connectivity. It utilizes second-order approximation to estimate the performance of discrete architectural candidates and converts learned continuous weights into discrete graph structures through genotype-to-model mapping.
The library covers deep learning optimization for convolutional and recurrent layers, including tools for training and evaluating discovered genotypes against standard benchmarks. It also provides utilities for loading pretrained weights for inference and generating graphical visualizations of neural cell connectivity.
Features
- Neural Architecture Search - Provides a framework for automatically discovering optimal neural network architectures using gradient-based optimization.
- Neural Topology Optimizers - Finds high-performing cell topologies for convolutional and recurrent layers using second-order approximation.
- Differentiable Super-nets - Implements a differentiable cell super-net to identify effective network connectivity through parallel operation training.
- Second-Order Optimizers - Uses second-order approximation and Hessian-based information to estimate the performance of architectural candidates.
- Weight Sharing Strategies - Employs weight-sharing mechanisms where a single set of weights is used across all candidate operations to accelerate discovery.
- Architecture Search Frameworks - Provides a differentiable framework for automating the discovery and optimization of neural model architectures.
- Convolutional Neural Networks - Automates the discovery of cell structures specifically for convolutional neural networks.
- Neural Cell Primitives - Provides a set of basic convolutional and recurrent operations to serve as the search space for architecture discovery.
- Deep Learning Research - Serves as a research tool for training, evaluating, and visualizing discovered neural network genotypes.
- Genotype-to-Model Mappings - Converts learned continuous weights into discrete graph structures to instantiate full-sized neural networks.
- Deep Learning Optimization - Tunes the layers and connectivity of neural networks to optimize performance on specific datasets.
- Model Performance Benchmarking - Evaluates discovered neural genotypes against standard benchmarks to verify final accuracy and error rates.
- Recurrent Neural Networks - Searches for effective connectivity patterns in recurrent neural networks to optimize sequence processing.
- Image Classification Architectures - Differentiable architecture search framework.
- Model Optimization and Search - Differentiable architecture search for efficient neural network design.
- Neural Architecture Search - Differentiable architecture search for neural networks.
- Neural Network Architectures - Differentiable architecture search.
- Architecture Search - Listed in the “Architecture Search” section of the The Incredible Pytorch awesome list.
Star history
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