# DeepSetNet: Predicting Sets with Deep Neural Networks

arXiv: https://arxiv.org/abs/1611.08998 · HTML: https://mlsanity.com/paper/1611.08998/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2016.

## Built on by (most cited first, 15 of 15)

- [DA-DETR: Domain Adaptive Detection Transformer with Information Fusion](https://mlsanity.com/paper/2103.17084/) (2021)
- [Learning Neural Set Functions Under the Optimal Subset Oracle](https://mlsanity.com/paper/2203.01693/) (2022)
- [DATE: Dual Assignment for End-to-End Fully Convolutional Object Detection](https://mlsanity.com/paper/2211.13859/) (2022)
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- [Neural Slot Interpreters: Grounding Object Semantics in Emergent Slot Representations](https://mlsanity.com/paper/2403.07887/) (2024)
- [Investigating transformers in the decomposition of polygonal shapes as point collections](https://mlsanity.com/paper/2108.07533/) (2021)
- [Deep set conditioned latent representations for action recognition](https://mlsanity.com/paper/2212.11030/) (2022)
- [Enhancing Neural Subset Selection: Integrating Background Information into Set Representations](https://mlsanity.com/paper/2402.03139/) (2024)
- [Improving Token-based Object Detection with Video](https://mlsanity.com/paper/2506.22562/) (2025)
- [Learning Attribute-Aware Hash Codes for Fine-Grained Image Retrieval via Query Optimization](https://mlsanity.com/paper/2509.17049/) (2025)
- [A Multi-scale Graph Signature for Persistence Diagrams based on Return Probabilities of Random Walks](https://mlsanity.com/paper/2209.14264/) (2022)
- [Joint Microseismic Event Detection and Location with a Detection Transformer](https://mlsanity.com/paper/2307.09207/) (2023)
- [Advancing Optimal Subset Oracle via Learning Relaxation of Neural Set Functions](https://mlsanity.com/paper/2607.11555/) (2026)
- [Geometric 2D Scene Graph Generation](https://mlsanity.com/paper/2607.22325/) (2026)
- [HEIR: Learning Human-Entity Interactions with Functional Roles](https://mlsanity.com/paper/2609.35955/) (2026)

## Abstract (arXiv)

This paper addresses the task of set prediction using deep learning. This is important because the output of many computer vision tasks, including image tagging and object detection, are naturally expressed as sets of entities rather than vectors. As opposed to a vector, the size of a set is not fixed in advance, and it is invariant to the ordering of entities within it. We define a likelihood for a set distribution and learn its parameters using a deep neural network. We also derive a loss for predicting a discrete distribution corresponding to set cardinality. Set prediction is demonstrated on the problem of multi-class image classification. Moreover, we show that the proposed cardinality loss can also trivially be applied to the tasks of object counting and pedestrian detection. Our approach outperforms existing methods in all three cases on standard datasets.