abailey81/deep-learning-cifar10-routing-net
Dynamic attention-weighted expert routing CNN for CIFAR-10 — multi-expert architecture, mixup augmentation, cosine LR scheduling, PyTorch.
What's novel
Dynamic attention-weighted expert routing CNN for CIFAR-10 — multi-expert architecture, mixup augmentation, cosine LR scheduling, PyTorch.
Code Analysis
0 files read · 4 roundsA standard PyTorch CIFAR-10 training script claiming to implement a complex RoutingNet architecture but failing to provide the actual implementation code.
Strengths
The project structure follows standard conventions with clear separation of models, training logic, and utilities. The README provides a detailed theoretical overview of the proposed architecture.
Weaknesses
The core source files containing the actual implementation logic are inaccessible or empty, preventing verification of the claimed 'RoutingNet' innovation. There is no test suite present in the repository.
Score Breakdown
Signal breakdown
Innovation
Craft
Traction
Scope
Evidence
Commits
6
Contributors
1
Files
21
Active weeks
2
Repository
Language
Python
Stars
2
Forks
0
License
MIT