Differentiable Architecture Search (DART) is a method for efficient architecture search. The search space is made continuous so that the architecture can be optimized with respect to its validation set performance through gradient descent.
Source: DARTS: Differentiable Architecture SearchPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Image Classification | 14 | 12.61% |
Reinforcement Learning (RL) | 6 | 5.41% |
Test | 5 | 4.50% |
Bilevel Optimization | 5 | 4.50% |
Language Modelling | 5 | 4.50% |
General Classification | 5 | 4.50% |
Network Pruning | 4 | 3.60% |
Classification | 4 | 3.60% |
BIG-bench Machine Learning | 4 | 3.60% |
Component | Type |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |