Search Results for author:
Found 4 papers, 1 papers with code
Expanding Expressiveness of Diffusion Models with Limited Data via Self-Distillation based Fine-Tuning
Training diffusion models on limited datasets poses challenges in terms of limited generation capacity and expressiveness, leading to unsatisfactory results in various downstream tasks utilizing pretrained diffusion models, such as domain translation and text-guided image manipulation.
Deep Cross-Modal Steganography Using Neural Representations
The proposed framework employs INRs to represent the secret data, which can handle data of various modalities and resolutions.
Reinforcement Learning-Based Black-Box Model Inversion Attacks
Model inversion attacks are a type of privacy attack that reconstructs private data used to train a machine learning model, solely by accessing the model.
Data Poisoning Attack Aiming the Vulnerability of Continual Learning
Training data generated by the proposed attack causes performance degradation on a specific task targeted by the attacker.
