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https://doi.org/10.2139/ssrn.4670298

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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-01234-2_49openalex-extracted
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
inferredcites →https://doi.org/10.1007/978-3-030-01267-0_9openalex-extracted
Deep Co-Training for Semi-Supervised Image Recognition
inferredcites →https://doi.org/10.1007/978-3-030-58610-2_26openalex-extracted
Multi-task Curriculum Framework for Open-Set Semi-supervised Learning
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-extracted
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2017.683openalex-extracted
Residual Attention Network for Image Classification
inferredcites →https://doi.org/10.1109/cvpr.2019.00521openalex-extracted
Label Propagation for Deep Semi-Supervised Learning
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00727openalex-extracted
Wavelet Integrated CNNs for Noise-Robust Image Classification
inferredcites →https://doi.org/10.1109/cvpr42600.2020.01096openalex-extracted
Generalized ODIN: Detecting Out-of-Distribution Image Without Learning From Out-of-Distribution Data
inferredcites →https://doi.org/10.1109/cvpr42600.2020.01401openalex-extracted
Deep Residual Flow for Out of Distribution Detection
inferredcites →https://doi.org/10.1109/cvpr46437.2021.00716openalex-extracted
All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-training
inferredcites →https://doi.org/10.1109/cvprw.2009.5206848openalex-extracted
ImageNet: A large-scale hierarchical image database
inferredcites →https://doi.org/10.1109/iccv.2019.00634openalex-extracted
Weakly Supervised Energy-Based Learning for Action Segmentation
inferredcites →https://doi.org/10.1109/ijcnn48605.2020.9207304openalex-extracted
Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning
inferredcites →https://doi.org/10.1109/tmm.2022.3179895openalex-extracted
They are Not Completely Useless: Towards Recycling Transferable Unlabeled Data for Class-Mismatched Semi-Supervised Learning
inferredcites →https://doi.org/10.1109/tsmc.1979.4310076openalex-extracted
A Threshold Selection Method from Gray-Level Histograms
inferredcites →https://doi.org/10.1609/aaai.v34i04.5763openalex-extracted
Semi-Supervised Learning under Class Distribution Mismatch
inferredcites →https://doi.org/10.1609/aaai.v35i8.16852openalex-extracted
Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised Learning
inferredcites →https://doi.org/10.48550/arxiv.1412.6980openalex-extracted
Adam: A Method for Stochastic Optimization
inferredcites →https://doi.org/10.48550/arxiv.1506.03365openalex-extracted
LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop
inferredcites →https://doi.org/10.48550/arxiv.1610.02136openalex-extracted
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1610.02242openalex-extracted
Temporal Ensembling for Semi-Supervised Learning
inferredcites →https://doi.org/10.48550/arxiv.1703.01780openalex-extracted
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
inferredcites →https://doi.org/10.48550/arxiv.1711.09325openalex-extracted
Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples
inferredcites →https://doi.org/10.48550/arxiv.1802.02611openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.1803.05984openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.1803.07728openalex-extracted
Unsupervised Representation Learning by Predicting Image Rotations
inferredcites →https://doi.org/10.48550/arxiv.1804.09170openalex-extracted
Realistic Evaluation of Deep Semi-Supervised Learning Algorithms
inferredcites →https://doi.org/10.48550/arxiv.1807.03888openalex-extracted
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
inferredcites →https://doi.org/10.48550/arxiv.1810.04650openalex-extracted
Multi-Task Learning as Multi-Objective Optimization
inferredcites →https://doi.org/10.48550/arxiv.1904.12848openalex-extracted
Unsupervised Data Augmentation for Consistency Training
inferredcites →https://doi.org/10.48550/arxiv.1905.02249openalex-extracted
MixMatch: A Holistic Approach to Semi-Supervised Learning
inferredcites →https://doi.org/10.48550/arxiv.1909.11480openalex-extracted
INPUT COMPLEXITY AND OUT-OF-DISTRIBUTION DETECTION WITH LIKELIHOOD-BASED GENERATIVE MODELS
inferredcites →https://doi.org/10.48550/arxiv.1909.13155openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.2001.07685openalex-extracted
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
inferredcites →https://doi.org/10.48550/arxiv.2002.11297openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.2005.03337openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.2010.06808openalex-extracted
Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout
inferredcites →https://doi.org/10.48550/arxiv.2011.14654openalex-extracted
Feature Space Singularity for Out-of-Distribution Detection
inferredcites →https://doi.org/10.48550/arxiv.2106.09022openalex-extracted
A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection
inferredcites →https://doi.org/10.48550/arxiv.2109.14162openalex-extracted
Can multi-label classification networks know what they don't know?
inferredcites →https://doi.org/10.48550/arxiv.2110.14048openalex-extracted
Conflict-Averse Gradient Descent for Multi-task Learning
inferredcites →https://doi.org/10.48550/arxiv.2111.12797openalex-extracted
ReAct: Out-of-distribution Detection With Rectified Activations
inferredcites →https://doi.org/10.5244/c.30.87openalex-extracted
Wide Residual Networks
inferredcites →https://doi.org/10.57702/zp44cu3gopenalex-extracted
Learning Multiple Layers of Features from Tiny Images
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