# Abstract Reasoning with Distracting Features

arXiv: https://arxiv.org/abs/1912.00569 · HTML: https://mlsanity.com/paper/1912.00569/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2019.

## Built on by (most cited first, 30 of 33)

- [Abstraction and Analogy-Making in Artificial Intelligence](https://mlsanity.com/paper/2102.10717/) (2021)
- [A Neuro-vector-symbolic Architecture for Solving Raven's Progressive Matrices](https://mlsanity.com/paper/2203.04571/) (2022)
- [Abstract Spatial-Temporal Reasoning via Probabilistic Abduction and Execution](https://mlsanity.com/paper/2103.14230/) (2021)
- [Deep Learning Methods for Abstract Visual Reasoning: A Survey on Raven's Progressive Matrices](https://mlsanity.com/paper/2201.12382/) (2022)
- [A Review of Emerging Research Directions in Abstract Visual Reasoning](https://mlsanity.com/paper/2202.10284/) (2022)
- [ACRE: Abstract Causal REasoning Beyond Covariation](https://mlsanity.com/paper/2103.14232/) (2021)
- [Learning Algebraic Representation for Systematic Generalization in Abstract Reasoning](https://mlsanity.com/paper/2111.12990/) (2021)
- [Effective Abstract Reasoning with Dual-Contrast Network](https://mlsanity.com/paper/2205.13720/) (2022)
- [Learning to reason over visual objects](https://mlsanity.com/paper/2303.02260/) (2023)
- [Selective Replay Enhances Learning in Online Continual Analogical Reasoning](https://mlsanity.com/paper/2103.03987/) (2021)
- [Unsupervised Abstract Reasoning for Raven's Problem Matrices](https://mlsanity.com/paper/2109.10011/) (2021)
- [Slot Abstractors: Toward Scalable Abstract Visual Reasoning](https://mlsanity.com/paper/2403.03458/) (2024)
- [SAViR-T: Spatially Attentive Visual Reasoning with Transformers](https://mlsanity.com/paper/2206.09265/) (2022)
- [Abstract Visual Reasoning: An Algebraic Approach for Solving Raven's Progressive Matrices](https://mlsanity.com/paper/2303.11730/) (2023)
- [Interactive Visual Reasoning under Uncertainty](https://mlsanity.com/paper/2206.09203/) (2022)
- [Abstract Reasoning via Logic-guided Generation](https://mlsanity.com/paper/2107.10493/) (2021)
- [Two-stage Rule-induction Visual Reasoning on RPMs with an Application to Video Prediction](https://mlsanity.com/paper/2111.12301/) (2021)
- [Multi-Viewpoint and Multi-Evaluation with Felicitous Inductive Bias Boost Machine Abstract Reasoning Ability](https://mlsanity.com/paper/2210.14914/) (2022)
- [Abstracting Concept-Changing Rules for Solving Raven's Progressive Matrix Problems](https://mlsanity.com/paper/2307.07734/) (2023)
- [One Self-Configurable Model to Solve Many Abstract Visual Reasoning Problems](https://mlsanity.com/paper/2312.09997/) (2023)
- [A Cognitively-Inspired Neural Architecture for Visual Abstract Reasoning Using Contrastive Perceptual and Conceptual Processing](https://mlsanity.com/paper/2309.10532/) (2023)
- [Sandra -- A Neuro-Symbolic Reasoner Based On Descriptions And Situations](https://mlsanity.com/paper/2402.00591/) (2024)
- [Learning Visual Abstract Reasoning through Dual-Stream Networks](https://mlsanity.com/paper/2411.19451/) (2024)
- [Data augmentation by morphological mixup for solving Raven's Progressive Matrices](https://mlsanity.com/paper/2103.05222/) (2021)
- [Towards Generative Abstract Reasoning: Completing Raven's Progressive Matrix via Rule Abstraction and Selection](https://mlsanity.com/paper/2401.09966/) (2024)
- [Triple-CFN: Separating Concepts and Features Enhances Machine Abstract Reasoning Ability](https://mlsanity.com/paper/2403.03190/) (2024)
- [Funny-Valen-Tine: Planning Solution Distribution Enhances Machine Abstract Reasoning Ability](https://mlsanity.com/paper/2407.02688/) (2024)
- [Systematic Abductive Reasoning via Diverse Relation Representations in Vector-symbolic Architecture](https://mlsanity.com/paper/2501.11896/) (2025)
- [A One-Shot Texture-Perceiving Generative Adversarial Network for Unsupervised Surface Inspection](https://mlsanity.com/paper/2106.06792/) (2021)
- [Deep Non-Monotonic Reasoning for Visual Abstract Reasoning Tasks](https://mlsanity.com/paper/2302.07137/) (2023)

## Abstract (arXiv)

Abstraction reasoning is a long-standing challenge in artificial intelligence. Recent studies suggest that many of the deep architectures that have triumphed over other domains failed to work well in abstract reasoning. In this paper, we first illustrate that one of the main challenges in such a reasoning task is the presence of distracting features, which requires the learning algorithm to leverage counterevidence and to reject any of the false hypotheses in order to learn the true patterns. We later show that carefully designed learning trajectory over different categories of training data can effectively boost learning performance by mitigating the impacts of distracting features. Inspired by this fact, we propose feature robust abstract reasoning (FRAR) model, which consists of a reinforcement learning based teacher network to determine the sequence of training and a student network for predictions. Experimental results demonstrated strong improvements over baseline algorithms and we are able to beat the state-of-the-art models by 18.7% in the RAVEN dataset and 13.3% in the PGM dataset.