# AGA: Attribute Guided Augmentation

arXiv: https://arxiv.org/abs/1612.02559 · HTML: https://mlsanity.com/paper/1612.02559/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2016.

## Built on by (most cited first, 10 of 10)

- [Learning to Focus: Cascaded Feature Matching Network for Few-shot Image Recognition](https://mlsanity.com/paper/2101.05018/) (2021)
- [I Want This Product but Different : Multimodal Retrieval with Synthetic Query Expansion](https://mlsanity.com/paper/2102.08871/) (2021)
- [Limitations of Face Image Generation](https://mlsanity.com/paper/2309.07277/) (2023)
- [Practical Continual Forgetting for Pre-trained Vision Models](https://mlsanity.com/paper/2501.09705/) (2025)
- [Enhancing Fine-Grained Classification for Low Resolution Images](https://mlsanity.com/paper/2105.00241/) (2021)
- [Multi-Agent Semi-Siamese Training for Long-tail and Shallow Face Learning](https://mlsanity.com/paper/2105.04113/) (2021)
- [Shaping Visual Representations with Attributes for Few-Shot Recognition](https://mlsanity.com/paper/2112.06398/) (2021)
- [CLR-GAM: Contrastive Point Cloud Learning with Guided Augmentation and Feature Mapping](https://mlsanity.com/paper/2302.14306/) (2023)
- [Did Models Learn Sufficiently? Attribution-Guided Training via Subset-Selected Counterfactual Augmentation](https://mlsanity.com/paper/2511.12100/) (2025)
- [DeltaGAN: Towards Diverse Few-shot Image Generation with Sample-Specific Delta](https://mlsanity.com/paper/2207.10271/) (2022)

## Abstract (arXiv)

We consider the problem of data augmentation, i.e., generating artificial samples to extend a given corpus of training data. Specifically, we propose attributed-guided augmentation (AGA) which learns a mapping that allows to synthesize data such that an attribute of a synthesized sample is at a desired value or strength. This is particularly interesting in situations where little data with no attribute annotation is available for learning, but we have access to a large external corpus of heavily annotated samples. While prior works primarily augment in the space of images, we propose to perform augmentation in feature space instead. We implement our approach as a deep encoder-decoder architecture that learns the synthesis function in an end-to-end manner. We demonstrate the utility of our approach on the problems of (1) one-shot object recognition in a transfer-learning setting where we have no prior knowledge of the new classes, as well as (2) object-based one-shot scene recognition. As external data, we leverage 3D depth and pose information from the SUN RGB-D dataset. Our experiments show that attribute-guided augmentation of high-level CNN features considerably improves one-shot recognition performance on both problems.