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Found 7 papers, 6 papers with code
Mitigating Label Noise through Data Ambiguation
Label noise poses an important challenge in machine learning, especially in deep learning, in which large models with high expressive power dominate the field.
Detecting Novelties with Empty Classes
We thereafter extend the DNN by $k$ empty classes and fine-tune it on the OoD data samples.
Memorization-Dilation: Modeling Neural Collapse Under Label Noise
The notion of neural collapse refers to several emergent phenomena that have been empirically observed across various canonical classification problems.
Conformal Credal Self-Supervised Learning
One such method, so-called credal self-supervised learning, maintains pseudo-supervision in the form of sets of (instead of single) probability distributions over labels, thereby allowing for a flexible yet uncertainty-aware labeling.
Kronecker Decomposition for Knowledge Graph Embeddings
Our experiments suggest that applying Kronecker decomposition on embedding matrices leads to an improved parameter efficiency on all benchmark datasets.
Credal Self-Supervised Learning
In our approach, we therefore allow the learner to label instances in the form of credal sets, that is, sets of (candidate) probability distributions.
Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce Model
In many real-world applications, the relative depth of objects in an image is crucial for scene understanding.
