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Found 6 papers, 6 papers with code
CASE: Commonsense-Augmented Score with an Expanded Answer Space
One of the major limitations of the basic score is that it treats all words as equally important.
RestNet: Boosting Cross-Domain Few-Shot Segmentation with Residual Transformation Network
Cross-domain few-shot segmentation (CD-FSS) aims to achieve semantic segmentation in previously unseen domains with a limited number of annotated samples.
Semi-supervised Domain Adaptation via Prototype-based Multi-level Learning
At the inter-domain level, we propose a cross-domain alignment loss to help the model use the target prototype for cross-domain knowledge transfer.
Hard Sample Aware Noise Robust Learning for Histopathology Image Classification
In this work, we introduce a novel hard sample aware noise robust learning method for histopathology image classification.
Ranked #1 on
Learning with noisy labels
on Chaoyang
Sample Prior Guided Robust Model Learning to Suppress Noisy Labels
Imperfect labels are ubiquitous in real-world datasets and seriously harm the model performance.
Ranked #1 on
Learning with noisy labels
on CIFAR-100N
A Semantic-based Method for Unsupervised Commonsense Question Answering
In this paper, we present a novel SEmantic-based Question Answering method (SEQA) for unsupervised commonsense question answering.
