# data augmentation

Published articles for data augmentation.

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## Granite 4.0 3B Vision: Compact Multimodal Intelligence for Enterprise Documents

DevFeed: [Granite 4.0 3B Vision: Compact Multimodal Intelligence for Enterprise Documents](<https://devfeed.tech/articles/granite-4-0-3b-vision-compact-multimodal-intelligence-for-enterprise-documents-7259.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/ibm-granite/granite-4-vision>)

Author: Madison Lee; Rogerio Feris; Eli Schwartz; Dhiraj Joshi; Pengyuan Li; Isaac Sanchez

Published: 2026-03-31T15:10:41Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [multimodal](<https://devfeed.tech/topics/multimodal.md>), [dataset](<https://devfeed.tech/topics/dataset.md>), [synthetic-data](<https://devfeed.tech/topics/synthetic-data.md>), [lora](<https://devfeed.tech/topics/lora.md>), [Language models](<https://devfeed.tech/topics/language-models.md>), [enterprise deployment](<https://devfeed.tech/topics/enterprise-deployment.md>)

Tags: [code](<https://devfeed.tech/tags/code.md>), [data](<https://devfeed.tech/tags/data.md>), [data-augmentation](<https://devfeed.tech/tags/data-augmentation.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [enterprise-deployment](<https://devfeed.tech/tags/enterprise-deployment.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [lora](<https://devfeed.tech/tags/lora.md>), [model](<https://devfeed.tech/tags/model.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [training](<https://devfeed.tech/tags/training.md>), [vision](<https://devfeed.tech/tags/vision.md>)

### AI overview

Granite 4.0 3B Vision is a compact multimodal model for enterprise document understanding. It extracts tables, interprets charts, and identifies semantic key-value pairs, using a LoRA adapter, visual-language capabilities, and the ChartNet dataset to support structured chart reasoning and document-processing pipelines.

### Source excerpt

- Table Extraction: Accurately parsing complex table structures (e.g., multi-row, multi-column, etc.) from document images - Chart Understanding: Converting charts and figures into structured machine-readable formats, summaries, or executable code - Semantic Key-Value Pair (KVP) Extraction: Identifying and grounding semantically meaningful key-value field pairs across diverse document layouts The model ships as a LoRA adapter on top of Granite 4.0 Micro, our dense language model, keeping...

## Introducing TextImage Augmentation for Document Images

DevFeed: [Introducing TextImage Augmentation for Document Images](<https://devfeed.tech/articles/introducing-textimage-augmentation-for-document-images-7172.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/doc_aug_hf_alb>)

Author: Dana Aubakirova; Pablo Montalvo; Vladimir Iglovikov

Published: 2024-08-06T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [data augmentation](<https://devfeed.tech/topics/data-augmentation.md>), [albumentations](<https://devfeed.tech/topics/albumentations.md>), [multimodal](<https://devfeed.tech/topics/multimodal.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [albumentations](<https://devfeed.tech/tags/albumentations.md>), [data-augmentation](<https://devfeed.tech/tags/data-augmentation.md>), [dataset](<https://devfeed.tech/tags/dataset.md>), [document-ai](<https://devfeed.tech/tags/document-ai.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [research](<https://devfeed.tech/tags/research.md>), [synthetic-data](<https://devfeed.tech/tags/synthetic-data.md>), [text-generation](<https://devfeed.tech/tags/text-generation.md>), [training](<https://devfeed.tech/tags/training.md>), [vision](<https://devfeed.tech/tags/vision.md>), [vlms](<https://devfeed.tech/tags/vlms.md>)

### AI overview

The article introduces a multimodal data augmentation pipeline for document images used in Vision Language Model fine-tuning. Developed with Albumentations AI, it modifies document images and their text annotations together while aiming to preserve text quality. The methods include text insertion, deletion, swapping, and stopword replacement, followed by image masking and inpainting.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Hello Deep Learning: Dropout, data augmentation, weight decay and quantisation

DevFeed: [Hello Deep Learning: Dropout, data augmentation, weight decay and quantisation](<https://devfeed.tech/articles/hello-deep-learning-dropout-data-augmentation-weight-decay-and-quantisation-36368.md>)

Original publisher: [Read original article](<https://berthub.eu/articles/posts/dropout-data-augmentation-weight-decay/>)

Published: 2023-03-30T10:00:07Z

Content type: tutorial

Language: en

Sources: [Bert Hubert's writings](<https://devfeed.tech/sources/bert-hubert-s-writings.md>)

Topics: [Deep learning](<https://devfeed.tech/topics/deep-learning.md>), [data augmentation](<https://devfeed.tech/topics/data-augmentation.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Image](<https://devfeed.tech/topics/image.md>)

Tags: [data-augmentation](<https://devfeed.tech/tags/data-augmentation.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [image](<https://devfeed.tech/tags/image.md>), [performance](<https://devfeed.tech/tags/performance.md>), [training](<https://devfeed.tech/tags/training.md>), [training-data](<https://devfeed.tech/tags/training-data.md>), [validation](<https://devfeed.tech/tags/validation.md>)

### AI overview

This Hello Deep Learning tutorial examines how data augmentation and related techniques can improve the robustness of a convolutional handwritten-digit recognition network. It explains that exposing the network to shifted, corrupted, or otherwise modified inputs helps it handle real-world variation and can enlarge a limited training set.

### Source excerpt

This page is part of the Hello Deep Learning series of blog posts. You are very welcome to improve this page via GitHub! In the previous chapter we found ways to speed up our character recognition learning by a factor of 20 by using a better optimizer, and a further factor of four by cleverly using threads using a 'shared nothing architecture'. We also learned how we can observe the development of parameters.

## A GPU-Free, From-Scratch Introduction to Modern Machine Learning

DevFeed: [A GPU-Free, From-Scratch Introduction to Modern Machine Learning](<https://devfeed.tech/articles/hello-deep-learning-36424.md>)

Original publisher: [Read original article](<https://berthub.eu/articles/posts/hello-deep-learning/>)

Published: 2023-03-30T09:59:00Z

Content type: tutorial

Language: en

Sources: [Bert Hubert's writings](<https://devfeed.tech/sources/bert-hubert-s-writings.md>)

Topics: [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Deep learning](<https://devfeed.tech/topics/deep-learning.md>), [Deep neural networks](<https://devfeed.tech/topics/deep-neural-networks.md>), [Code](<https://devfeed.tech/topics/code.md>), [data augmentation](<https://devfeed.tech/topics/data-augmentation.md>), [Transformers](<https://devfeed.tech/topics/transformers.md>)

Tags: [code](<https://devfeed.tech/tags/code.md>), [data-augmentation](<https://devfeed.tech/tags/data-augmentation.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [neural-networks](<https://devfeed.tech/tags/neural-networks.md>), [transformers](<https://devfeed.tech/tags/transformers.md>), [tutorial](<https://devfeed.tech/tags/tutorial.md>)

### AI overview

A GPU-free tutorial that explains modern machine learning and deep learning from the ground up, developing neural networks and a practical demo from scratch.

### Source excerpt

A from scratch GPU-free introduction to modern machine learning. Many tutorials exist already of course, but this one aims to really explain what is going on, from the ground up. Also, we'll develop the demo until it is actually useful on real life data which you can supply yourself. Other documents start out from the (very impressive) PyTorch environment, or they attempt to math it up from first principles. Trying to understand deep learning via PyTorch is like trying to learn aerodynamics from flying an Airbus A380.