2020.10.6
NIPS-2020 1900 accepted papers
(一般在 author notification 后过几天放出来）
粗略搜索了一些相关的论文
Interesting paper
- Self-Distillation as Instance-Specific Label Smoothing
- Provably Consistent Partial-Label Learning
- Learning from Label Proportions: A Mutual Contamination Framework
- Rethinking Importance Weighting for Deep Learning under Distribution Shift
Noisy labels
- Parts-dependent Label Noise: Towards Instance-dependent Label Noise
- Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning
- Identifying Mislabeled Data using the Area Under the Margin Ranking
- Coresets for Robust Training of Deep Neural Networks against Noisy Labels
- Early-Learning Regularization Prevents Memorization of Noisy Labels
- A Topological Filter for Learning with Label Noise
- What Do Neural Networks Learn When Trained With Random Labels?
Class-imbalance Learning
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MESA: Effective Ensemble Imbalanced Learning with MEta-SAmpler
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Posterior Re-calibration for Imbalanced Datasets
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Generative Modeling of Factorized Representations in Class-Imbalanced Data
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Rethinking the Value of Labels for Improving Class-Imbalanced Learning
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Devil in the Momentum: Long-Tailed Classification by Removing Momentum Causal Effect
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Balanced Meta-Softmax for Long-Tailed Visual Recognition
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What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation
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Fast Unbalanced Optimal Transport on Tree
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Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning (glz 12-组会)
PU Learning
- Learning from Positive and Unlabeled Data with Arbitrary Positive Shift
- A Variational Approach for Learning from Positive and Unlabeled Data
- Semi-Supervised Partial Label Learning via Confidence-Rated Margin Maximization
- Temporal Positive-unlabeled Learning for Biomedical Hypothesis Generation via Risk Estimation
- Partial Optimal Transport with applications on Positive-Unlabeled Learning
Domain Adaptation:
- Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift
Model calibration
- Improving model calibration with accuracy versus uncertainty optimization
点云，3D 重建
- CaSPR: Learning Canonical Spatiotemporal Point Cloud Representations
- Group Contextual Encoding for 3D Point Clouds
- PIE-NET: Parametric Inference of Point Cloud Edges
- Rotation-Invariant Local-to-Global Representation Learning for 3D Point Cloud
- Self-Supervised Few-Shot Learning on Point Clouds