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Found 5 papers, 2 papers with code
PM2: A New Prompting Multi-modal Model Paradigm for Few-shot Medical Image Classification
The other is to classification on feature distribution of visual tokens from vision encoder.
Label Embedding via Low-Coherence Matrices
Label embedding is a framework for multiclass classification problems where each label is represented by a distinct vector of some fixed dimension, and training involves matching model output to the vector representing the correct label.
Learning from Label Proportions by Learning with Label Noise
Learning from label proportions (LLP) is a weakly supervised classification problem where data points are grouped into bags, and the label proportions within each bag are observed instead of the instance-level labels.
Learning from Label Proportions: A Mutual Contamination Framework
Learning from label proportions (LLP) is a weakly supervised setting for classification in which unlabeled training instances are grouped into bags, and each bag is annotated with the proportion of each class occurring in that bag.
Learning from Multiple Corrupted Sources, with Application to Learning from Label Proportions
We study binary classification in the setting where the learner is presented with multiple corrupted training samples, with possibly different sample sizes and degrees of corruption, and introduce an approach based on minimizing a weighted combination of corruption-corrected empirical risks.
