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Found 5 papers, 2 papers with code
OOD Aware Supervised Contrastive Learning
Our solution is simple and efficient and acts as a natural extension of the closed-set supervised contrastive representation learning.
Contrastive Classification and Representation Learning with Probabilistic Interpretation
In this work, we investigate the possibility of learning both the representation and the classifier using one objective function that combines the robustness of contrastive learning and the probabilistic interpretation of cross entropy loss.
Continual Novelty Detection
This work identifies the crucial link between the two problems and investigates the Novelty Detection problem under the Continual Learning setting.
Road Anomaly Detection by Partial Image Reconstruction With Segmentation Coupling
To that end, we propose a reconstruction module that can be used with many existing semantic segmentation networks, and that is trained to recognize and reconstruct road (drivable) surface from a small bottleneck.
Identifying Wrongly Predicted Samples: A Method for Active Learning
State-of-the-art machine learning models require access to significant amount of annotated data in order to achieve the desired level of performance.
