Distribution Augmentation for Generative Modeling

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[ICML 2022] Improving Out-of-Distribution Robustness via Selective Augmentation

(5:40)

Negative Data Augmentation

(14:51)

AugMax Explained!

(36:30)

Fair and accurate age prediction using distribution aware data curation and augmentation

(4:54)

Silviu Pitis on Generalizing O.O.D. using Counterfactual Data Augmentation | Toronto AIR Seminar

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Oguzhan Gencoglu - Best Practices for Data Augmentation | PyData Helsinki

(36:39)

Learned data augmentation in natural language processing - Kyunghyun Cho

(1:11:15)

Image Augmentation for Imbalanced Medical Datasets

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[ECCV 2024] TAG: Text Prompt Augmentation for Zero-Shot Out-of-Distribution Detection

(7:16)

Stéphanie Allassonnière - Geometry-Aware Variational Autoencoders for Medical Data Augmentation

(55:45)

[ECCV 2024] PoseAugment: Generative Human Pose Data Augmentation with Physical Plausibility

(4:28)

Ex2: Neural Data Augmentation via Example Extrapolation (Research Paper Walkthrough)

(17:21)

2020 Distribution Augmentation

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Differentiable Augmentation for Data-Efficient GAN Training (AI Paper Summary)

(5:27)

Training GANs with Limited Data

(15:6)

Bayesian Variable Selection for Mixture ofLogistic Model with Pólya-Gamma Data Augmentation

(27:6)

MH-AUG: Metropolis-Hastings Data Augmentation for Graph Neural Networks (NeurIPS 2021)

(14:11)

Standardization vs Normalization Clearly Explained!

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