Search Results for author:
Found 4 papers, 3 papers with code
Everything to the Synthetic: Diffusion-driven Test-time Adaptation via Synthetic-Domain Alignment
To adapt the source model to the synthetic domain of the unconditional diffusion model, we introduce a Synthetic-Domain Alignment (SDA) framework to fine-tune the source model with synthetic data.
Probabilistic Contrastive Learning for Long-Tailed Visual Recognition
To overcome this obstacle, we propose a novel probabilistic contrastive (ProCo) learning algorithm that estimates the data distribution of the samples from each class in the feature space, and samples contrastive pairs accordingly.
Ranked #10 on
Long-tail Learning
on iNaturalist 2018
SimPro: A Simple Probabilistic Framework Towards Realistic Long-Tailed Semi-Supervised Learning
Recent advancements in semi-supervised learning have focused on a more realistic yet challenging task: addressing imbalances in labeled data while the class distribution of unlabeled data remains both unknown and potentially mismatched.
Assessing a Single Image in Reference-Guided Image Synthesis
For Reference-guided Image Synthesis (RIS) tasks, i. e., rendering a source image in the style of another reference image, where assessing the quality of a single generated image is crucial, these metrics are not applicable.
