Search Results for author:
Found 5 papers, 3 papers with code
FRDiff : Feature Reuse for Universal Training-free Acceleration of Diffusion Models
The substantial computational costs of diffusion models, especially due to the repeated denoising steps necessary for high-quality image generation, present a major obstacle to their widespread adoption.
Temporal Dynamic Quantization for Diffusion Models
The diffusion model has gained popularity in vision applications due to its remarkable generative performance and versatility.
Learning Fair Representation via Distributional Contrastive Disentanglement
We provide a new type of contrastive loss motivated by Gaussian and Student-t kernels for distributional contrastive learning with theoretical analysis.
NIPQ: Noise proxy-based Integrated Pseudo-Quantization
Recently, pseudoquantization training has been proposed as an alternative approach to updating the learnable parameters using the pseudo-quantization noise instead of STE.
Geodesic Multi-Modal Mixup for Robust Fine-Tuning
Such a lack of alignment and uniformity might restrict the transferability and robustness of embeddings.
