PCDM uses a poisoning-oriented conditional diffusion model with an adjustable vector and jumping strategy to create stealthier and more effective poisoned data than GAN-based attacks against federated learning.
Catastrophic forgetting and mode collapse in gans
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
Neural implicit functions enable resolution-agnostic, deterministic virtual staining from H&E to IHC images with SOTA results and better low-data performance than patch-based GAN or diffusion methods.
MSDformer introduces a multi-scale discrete transformer that tokenizes time series at multiple scales and models them autoregressively in discrete space, claiming superior performance over prior DTM methods with rate-distortion theoretical support.
citing papers explorer
-
PCDM: A Diffusion-Based Data Poisoning Attack Against Federated Learning Systems
PCDM uses a poisoning-oriented conditional diffusion model with an adjustable vector and jumping strategy to create stealthier and more effective poisoned data than GAN-based attacks against federated learning.
-
IMPLICITSTAINER: Resolution Agnostic Data-Efficient Virtual Staining Using Neural Implicit Functions
Neural implicit functions enable resolution-agnostic, deterministic virtual staining from H&E to IHC images with SOTA results and better low-data performance than patch-based GAN or diffusion methods.
-
MSDformer: Multi-scale Discrete Transformer For Time Series Generation
MSDformer introduces a multi-scale discrete transformer that tokenizes time series at multiple scales and models them autoregressively in discrete space, claiming superior performance over prior DTM methods with rate-distortion theoretical support.