End-to-End Multimodal Data Augmentation and Adversarial Robustness Benchmark with AugLy for Images, Text, Audio, and PyTorch
A tutorial shows AugLy augmentations and small robustness tests for image, text, and audio in PyTorch.
MarkTechPost published a self-contained tutorial that uses Meta's AugLy library to augment images, text, and audio and to measure robustness. The walkthrough covers functional and class APIs, metadata and intensity tracking, probabilistic composition, bounding-box-aware transforms, and custom transforms. It benchmarks perceptual-hash copy detection under image distortions and evaluates a small template-based sentiment classifier against adversarial text, Unicode obfuscation, sanitization, and adversarial training. The examples rely on synthetic data and connect AugLy transforms to PyTorch datasets and DataLoaders.
- Tutorial uses AugLy for image, text, and audio augmentation.
- Covers metadata, intensity, composition, and bounding-box transforms.
- Benchmarks perceptual-hash copy detection under image distortions.
- Tests a tiny text classifier against obfuscation and adversarial training.
- Experiments use synthetic data wired into PyTorch DataLoaders.
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From the original article
10 min read at marktechpost.com
Discover how to build a comprehensive multimodal augmentation and adversarial robustness workflow using AugLy for images, text, audio, and PyTorch datasets. The post End-to-End Multimodal Data Augmentation and Adversarial Robustness Benchmark with AugLy for Images, Text, Audio, and PyTorch appeared first on MarkTechPost.
By Sana Hassan · marktechpost.com
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