MNIST Classification
Cross-source consensus on MNIST Classification from 1 sources and 5 claims.
1 sources · 5 claims
Uses
Benefits
Risks & contraindications
Comparisons
Evidence quality
Highlighted claims
- The simulated three-layer classifier reaches 91.2% test accuracy on MNIST. — Photonic AI: A Hybrid Diffractive Holographic Neural System for Passive Optical Real-Time Image Classification
- The main experimental setup uses MNIST as a controlled benchmark for the diffractive stack. — Photonic AI: A Hybrid Diffractive Holographic Neural System for Passive Optical Real-Time Image Classification
- Scaling beyond MNIST is unresolved and may require larger or richer optical architectures. — Photonic AI: A Hybrid Diffractive Holographic Neural System for Passive Optical Real-Time Image Classification
- The MNIST result is interpreted as showing that passive diffractive propagation can separate handwritten digit class distributions despite its restricted computational form. — Photonic AI: A Hybrid Diffractive Holographic Neural System for Passive Optical Real-Time Image Classification
- The reported accuracy remains substantially below near-perfect electronic neural-network performance on MNIST. — Photonic AI: A Hybrid Diffractive Holographic Neural System for Passive Optical Real-Time Image Classification