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Found 9 papers, 2 papers with code
Domain Decorrelation with Potential Energy Ranking
PoER helps the neural networks to capture label-related features which contain the domain information first in shallow layers and then distills the label-discriminative representations out progressively, enforcing the neural networks to be aware of the characteristic of objects and background which is vital to the generation of domain-invariant features.
Exploring Domain Incremental Video Highlights Detection with the LiveFood Benchmark
However, most VHD methods are based on the closed world assumption, i. e., a fixed number of highlight categories is defined in advance and all training data are available beforehand.
Alleviating Mode Collapse in GAN via Diversity Penalty Module
We compare the proposed method with Unrolled GAN (Metz et al. 2016), BourGAN (Xiao, Zhong, and Zheng 2018), PacGAN (Lin et al. 2018), VEEGAN (Srivastava et al. 2017) and ALI (Dumoulin et al. 2016) on 2D synthetic dataset, and results show that the diversity penalty module can help GAN capture much more modes of the data distribution.
Boundary Aware Learning for Out-of-distribution Detection
This paper focuses on the problem of detecting out-of-distribution (ood) samples with neural nets.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Evaluating the impact of quarantine measures on COVID-19 spread
We generate counterfactual simulations to estimate effectiveness of quarantine measures.
Gradient Concealment: Free Lunch for Defending Adversarial Attacks
Recent studies show that the deep neural networks (DNNs) have achieved great success in various tasks.
Free Lunch for Generating Effective Outlier Supervision
Generally, existing approaches in dealing with out-of-distribution (OOD) detection mainly focus on the statistical difference between the features of OOD and in-distribution (ID) data extracted by the classifiers.
AutoMatch: A Large-scale Audio Beat Matching Benchmark for Boosting Deep Learning Assistant Video Editing
In this paper, we investigate a novel and practical problem, namely audio beat matching (ABM), which aims to recommend the proper transition time stamps based on the background music.
Image Background Serves as Good Proxy for Out-of-distribution Data
Out-of-distribution (OOD) detection empowers the model trained on the closed image set to identify unknown data in the open world.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
