# Data-Free Sketch-Based Image Retrieval

arXiv: https://arxiv.org/abs/2303.07775 · HTML: https://mlsanity.com/paper/2303.07775/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 42 of its 63 references that are in the map.
- First posted on arXiv 14 Mar 2023.

## Builds on (most cited first, 30 of 42)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination](https://mlsanity.com/paper/1805.01978/) (2018)
- [Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time](https://mlsanity.com/paper/2203.05482/) (2022)
- [Self-training with Noisy Student improves ImageNet classification](https://mlsanity.com/paper/1911.04252/) (2019)
- [Learning Robust Global Representations by Penalizing Local Predictive Power](https://mlsanity.com/paper/1905.13549/) (2019)
- [Relational Knowledge Distillation](https://mlsanity.com/paper/1904.05068/) (2019)
- [Contrastive Representation Distillation](https://mlsanity.com/paper/1910.10699/) (2019)
- [Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion](https://mlsanity.com/paper/1912.08795/) (2019)
- [Understanding Deep Image Representations by Inverting Them](https://mlsanity.com/paper/1412.0035/) (2014)
- [No Fuss Distance Metric Learning using Proxies](https://mlsanity.com/paper/1703.07464/) (2017)
- [Data-Free Learning of Student Networks](https://mlsanity.com/paper/1904.01186/) (2019)
- [Proxy Anchor Loss for Deep Metric Learning](https://mlsanity.com/paper/2003.13911/) (2020)
- [Learning Robust Representations via Multi-View Information Bottleneck](https://mlsanity.com/paper/2002.07017/) (2020)
- [Generalized Source-free Domain Adaptation](https://mlsanity.com/paper/2108.01614/) (2021)
- [Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning](https://mlsanity.com/paper/2203.09249/) (2022)
- [Data-Free Knowledge Distillation for Deep Neural Networks](https://mlsanity.com/paper/1710.07535/) (2017)
- [Zero-shot Knowledge Transfer via Adversarial Belief Matching](https://mlsanity.com/paper/1905.09768/) (2019)
- [Improving Black-box Adversarial Attacks with a Transfer-based Prior](https://mlsanity.com/paper/1906.06919/) (2019)
- [Data-Free Network Quantization With Adversarial Knowledge Distillation](https://mlsanity.com/paper/2005.04136/) (2020)
- [Doodle to Search: Practical Zero-Shot Sketch-based Image Retrieval](https://mlsanity.com/paper/1904.03451/) (2019)
- [A Zero-Shot Framework for Sketch-based Image Retrieval](https://mlsanity.com/paper/1807.11724/) (2018)
- [Deep Sketch Hashing: Fast Free-hand Sketch-Based Image Retrieval](https://mlsanity.com/paper/1703.05605/) (2017)
- [Contrastive Model Inversion for Data-Free Knowledge Distillation](https://mlsanity.com/paper/2105.08584/) (2021)
- [Towards Data-Free Model Stealing in a Hard Label Setting](https://mlsanity.com/paper/2204.11022/) (2022)

## Built on by (most cited first, 7 of 7)

- [ProS: Prompting-to-simulate Generalized knowledge for Universal Cross-Domain Retrieval](https://mlsanity.com/paper/2312.12478/) (2023)
- [Sparse Model Inversion: Efficient Inversion of Vision Transformers for Data-Free Applications](https://mlsanity.com/paper/2510.27186/) (2025)
- [Elevating All Zero-Shot Sketch-Based Image Retrieval Through Multimodal Prompt Learning](https://mlsanity.com/paper/2407.04207/) (2024)
- [SketchMind: A Multi-Agent Cognitive Framework for Assessing Student-Drawn Scientific Sketches](https://mlsanity.com/paper/2507.22904/) (2025)
- [Symmetrical Bidirectional Knowledge Alignment for Zero-Shot Sketch-Based Image Retrieval](https://mlsanity.com/paper/2312.10320/) (2023)
- [Zero-shot sketch-based remote sensing image retrieval based on multi-level and attention-guided tokenization](https://mlsanity.com/paper/2402.02141/) (2024)
- [DUDE: Diffusion-Based Unsupervised Cross-Domain Image Retrieval](https://mlsanity.com/paper/2509.04193/) (2025)

## Abstract (arXiv)

Rising concerns about privacy and anonymity preservation of deep learning models have facilitated research in data-free learning (DFL). For the first time, we identify that for data-scarce tasks like Sketch-Based Image Retrieval (SBIR), where the difficulty in acquiring paired photos and hand-drawn sketches limits data-dependent cross-modal learning algorithms, DFL can prove to be a much more practical paradigm. We thus propose Data-Free (DF)-SBIR, where, unlike existing DFL problems, pre-trained, single-modality classification models have to be leveraged to learn a cross-modal metric-space for retrieval without access to any training data. The widespread availability of pre-trained classification models, along with the difficulty in acquiring paired photo-sketch datasets for SBIR justify the practicality of this setting. We present a methodology for DF-SBIR, which can leverage knowledge from models independently trained to perform classification on photos and sketches. We evaluate our model on the Sketchy, TU-Berlin, and QuickDraw benchmarks, designing a variety of baselines based on state-of-the-art DFL literature, and observe that our method surpasses all of them by significant margins. Our method also achieves mAPs competitive with data-dependent approaches, all the while requiring no training data. Implementation is available at urlhttps://github.com/abhrac/data-free-sbir.