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Found 11 papers, 3 papers with code
All-around Neural Collapse for Imbalanced Classification
Neural Collapse (NC) presents an elegant geometric structure that enables individual activations (features), class means and classifier (weights) vectors to reach \textit{optimal} inter-class separability during the terminal phase of training on a \textit{balanced} dataset.
Dynamic Against Dynamic: An Open-set Self-learning Framework
In particular, a novel self-matching module is designed for OSSL, which can achieve the adaptation in automatically identifying known class samples while rejecting unknown class samples which are further utilized to enhance the discriminability of the model as the instantiated representation of unknown classes.
All Beings Are Equal in Open Set Recognition
In open-set recognition (OSR), a promising strategy is exploiting pseudo-unknown data outside given $K$ known classes as an additional $K$+$1$-th class to explicitly model potential open space.
Class-Aware Universum Inspired Re-Balance Learning for Long-Tailed Recognition
For these issues, we propose the Class-aware Universum Inspired Re-balance Learning(CaUIRL) for long-tailed recognition, which endows the Universum with class-aware ability to re-balance individual minority classes from both sample quantity and quality.
View-labels Are Indispensable: A Multifacet Complementarity Study of Multi-view Clustering
Consistency and complementarity are two key ingredients for boosting multi-view clustering (MVC).
Universum-inspired Supervised Contrastive Learning
In this paper, inspired by Universum Learning which uses out-of-class samples to assist the target tasks, we investigate Mixup from a largely under-explored perspective - the potential to generate in-domain samples that belong to none of the target classes, that is, universum.
Leave Zero Out: Towards a No-Cross-Validation Approach for Model Selection
On the one hand, for small data cases, CV suffers a conservatively biased estimation, since some part of the limited data has to hold out for validation.
A Multi-view Perspective of Self-supervised Learning
Specifically, a simple multi-view learning framework is specially designed (SSL-MV), which assists the feature learning of downstream tasks (original view) through the same tasks on the augmented views.
Visual and Semantic Prototypes-Jointly Guided CNN for Generalized Zero-shot Learning
On the other hand, for G-OSR, introducing such semantic information of known classes not only improves the recognition performance but also endows OSR with the cognitive ability of unknown classes.
Recent Advances in Open Set Recognition: A Survey
A more realistic scenario is open set recognition (OSR), where incomplete knowledge of the world exists at training time, and unknown classes can be submitted to an algorithm during testing, requiring the classifiers to not only accurately classify the seen classes, but also effectively deal with the unseen ones.
Collective decision for open set recognition
In open set recognition (OSR), almost all existing methods are designed specially for recognizing individual instances, even these instances are collectively coming in batch.
