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Found 6 papers, 5 papers with code
Unknown Domain Inconsistency Minimization for Domain Generalization
In particular, by aligning the loss landscape acquired in the source domain to the loss landscape of perturbed domains, we expect to achieve generalization grounded on these flat minima for the unknown domains.
Dirichlet-based Per-Sample Weighting by Transition Matrix for Noisy Label Learning
We propose good utilization of the transition matrix is crucial and suggest a new utilization method based on resampling, coined RENT.
Label-Noise Robust Diffusion Models
This paper proposes Transition-aware weighted Denoising Score Matching (TDSM) for training conditional diffusion models with noisy labels, which is the first study in the line of diffusion models.
Make Prompts Adaptable: Bayesian Modeling for Vision-Language Prompt Learning with Data-Dependent Prior
This paper presents a Bayesian-based framework of prompt learning, which could alleviate the overfitting issues on few-shot learning application and increase the adaptability of prompts on unseen instances.
Loss-Curvature Matching for Dataset Selection and Condensation
Training neural networks on a large dataset requires substantial computational costs.
From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model
We suggest a new branch of method, Noisy Prediction Calibration (NPC) in learning with noisy labels.
