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Found 8 papers, 4 papers with code
Learning Transferable Negative Prompts for Out-of-Distribution Detection
Existing prompt learning methods have shown certain capabilities in Out-of-Distribution (OOD) detection, but the lack of OOD images in the target dataset in their training can lead to mismatches between OOD images and In-Distribution (ID) categories, resulting in a high false positive rate.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Learning Dynamic Tetrahedra for High-Quality Talking Head Synthesis
Recent works in implicit representations, such as Neural Radiance Fields (NeRF), have advanced the generation of realistic and animatable head avatars from video sequences.
Out-of-Distribution Detection in Long-Tailed Recognition with Calibrated Outlier Class Learning
To this end, we introduce a novel calibrated outlier class learning (COCL) approach, in which 1) a debiased large margin learning method is introduced in the outlier class learning to distinguish OOD samples from both head and tail classes in the representation space and 2) an outlier-class-aware logit calibration method is defined to enhance the long-tailed classification confidence.
Learning Adversarial Semantic Embeddings for Zero-Shot Recognition in Open Worlds
Open-Set Recognition (OSR) is dedicated to addressing the unknown class issue, but existing OSR methods are not designed to model the semantic information of the unseen classes.
Towards Adversarial Robustness via Transductive Learning
Compared to traditional "test-time" defenses, these defense mechanisms "dynamically retrain" the model based on test time input via transductive learning; and theoretically, attacking these defenses boils down to bilevel optimization, which seems to raise the difficulty for adaptive attacks.
Test-Time Adaptation and Adversarial Robustness
On the positive side, we show that, if one is allowed to access the training data, then Domain Adversarial Neural Networks (${\sf DANN}$), an algorithm designed for unsupervised domain adaptation, can provide nontrivial robustness in the test-time maximin threat model against strong transfer attacks and adaptive fixed point attacks.
Improving Multi-Person Pose Estimation using Label Correction
If a model is trained with data including such missing labels, the output of the model for the location, even though it is correct, is penalized as a false positive, which is likely to cause negative effects on the performance of the model.
Full-body High-resolution Anime Generation with Progressive Structure-conditional Generative Adversarial Networks
Our method tackles the limitations by progressively increasing the resolution of both generated images and structural conditions during training.
