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Found 8 papers, 1 papers with code
How Does Bayes Error Limit Probabilistic Robust Accuracy
This reduced Bayes uncertainty allows a higher upper bound on probabilistic robust accuracy than that on deterministic robust accuracy.
Certified Robust Accuracy of Neural Networks Are Bounded due to Bayes Errors
We first show that the accuracy inevitably decreases in the pursuit of robustness due to changed Bayes error in the altered data distribution.
HCL-TAT: A Hybrid Contrastive Learning Method for Few-shot Event Detection with Task-Adaptive Threshold
Conventional event detection models under supervised learning settings suffer from the inability of transfer to newly-emerged event types owing to lack of sufficient annotations.
Representing Affect Information in Word Embeddings
A growing body of research in natural language processing (NLP) and natural language understanding (NLU) is investigating human-like knowledge learned or encoded in the word embeddings from large language models.
MIPR:Automatic Annotation of Medical Images with Pixel Rearrangement
In this paper, inspired by self-training of semi-supervised learning, we pro? pose a novel approach to solve the lack of annotated data from another angle, called medical image pixel rearrangement (short in MIPR).
Instance Segmentation of Unlabeled Modalities via Cyclic Segmentation GAN
Instance segmentation for unlabeled imaging modalities is a challenging but essential task as collecting expert annotation can be expensive and time-consuming.
Soft-CP: A Credible and Effective Data Augmentation for Semantic Segmentation of Medical Lesions
The medical datasets are usually faced with the problem of scarcity and data imbalance.
Invertible Concept-based Explanations for CNN Models with Non-negative Concept Activation Vectors
Based on the requirements of fidelity (approximate models to target models) and interpretability (being meaningful to people), we design measurements and evaluate a range of matrix factorization methods with our framework.
