Duration: (4:59) ?Subscribe5835 2025-02-19T14:23:08+00:00
Why RandAugment is the best Data Augmentation technique
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RandAugment Explained!
(8:15)
RandAugment Paper Walkthrough
(13:11)
RandAugment | Lecture 17 (Part 1) | Applied Deep Learning (Supplementary)
(10:32)
RandAugment Practical automated data augmentation with a reduced search space
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AugMax Explained!
(36:30)
TrivialAugment- Tuning-free yet SOTA Data Augmentation. Machine Learning Made Simple. Devansh
(11:15)
DLFVC - 13 - Data Augmentation
(44:6)
AutoAugment | Lecture 16 (Part 4) | Applied Deep Learning (Supplementary)
Introduction to Deep Learning (I2DL 2023) - 8. Augmentation and Regularization
(1:4:48)
Data augmentation to address overfitting | Deep Learning Tutorial 26 (Tensorflow, Keras \u0026 Python)
(31:33)
How to Approach Model Optimization for AutoML
(10:35)
Adversarial Robustness
(30:55)
Supervised Contrastive Learning
(30:8)
TensorFlow Tutorial 13 - Data Augmentation
(15:40)
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
(20:12)
Pytorch Data Augmentation using Torchvision
(9:40)
Waymo at ECCV 2022 | Scaling 3D Detection to the Long Tail
(40:3)
Effective Data Augmentation With Diffusion Models [NeurIPS 2023]
(24:6)
Data Augmentation for Object Detection
(14:18)
Easy Data Augmentation for Text Classification
(15:32)
TrivialAugment, ICCV '21 Oral, Samuel Müller \u0026 Frank Hutter
(11:58)
Cutout Augmentation
(7:5)
DeiT - Data-efficient image transformers \u0026 distillation through attention (paper illustrated)
(10:22)
Stride Random Erasing Augmentation
(14:3)
Distribution Augmentation for Generative Modeling
(19:7)
150 - Warning about keras' data augmentation when working with categorical labels
(10:59)
MODALS: Modality-agnostic Automated Data Augmentation in the Latent Space
(26:27)
Hossein Mobahi: \
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