Drifting Models
Cross-source consensus on Drifting Models from 1 sources and 4 claims.
1 sources · 4 claims
How it works
Comparisons
Highlighted claims
- Drifting models generate samples with a single generator evaluation. — DriftXpress: Faster Drifting Models via Projected RKHS Fields
- Their training signal uses attraction toward data and repulsion away from the current model distribution in feature space. — DriftXpress: Faster Drifting Models via Projected RKHS Fields
- Standard drifting avoids adversarial discriminator training but shifts substantial computation into kernel-based field estimation during training. — DriftXpress: Faster Drifting Models via Projected RKHS Fields
- The exact drifting field is anti-symmetric and becomes zero when the data and model distributions match. — DriftXpress: Faster Drifting Models via Projected RKHS Fields