# L-WISE: Boosting Human Visual Category Learning Through Model-Based Image Selection and Enhancement arXiv: https://arxiv.org/abs/2412.09765 ยท HTML: https://mlsanity.com/paper/2412.09765/ ## Key facts - Cited by 2 papers in the map (data through 14 Oct 2026). - Builds on 30 of its 56 references that are in the map. - First posted on arXiv 12 Dec 2024. ## Builds on (most cited first, 30 of 30) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020) - [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014) - [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017) - [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019) - [Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks](https://mlsanity.com/paper/2003.01690/) (2020) - [Differentiable Augmentation for Data-Efficient GAN Training](https://mlsanity.com/paper/2006.10738/) (2020) - [XCiT: Cross-Covariance Image Transformers](https://mlsanity.com/paper/2106.09681/) (2021) - [Benchmarking Representation Learning for Natural World Image Collections](https://mlsanity.com/paper/2103.16483/) (2021) - [Deep Learning Through the Lens of Example Difficulty](https://mlsanity.com/paper/2106.09647/) (2021) - [Disparities in Dermatology AI: Assessments Using Diverse Clinical Images](https://mlsanity.com/paper/2111.08006/) (2021) - [Characterizing Structural Regularities of Labeled Data in Overparameterized Models](https://mlsanity.com/paper/2002.03206/) (2020) - [A Deep Journey into Super-resolution: A survey](https://mlsanity.com/paper/1904.07523/) (2019) - [A Petri Dish for Histopathology Image Analysis](https://mlsanity.com/paper/2101.12355/) (2021) - [A Light Recipe to Train Robust Vision Transformers](https://mlsanity.com/paper/2209.07399/) (2022) - [Harmonizing the object recognition strategies of deep neural networks with humans](https://mlsanity.com/paper/2211.04533/) (2022) - [Unadversarial Examples: Designing Objects for Robust Vision](https://mlsanity.com/paper/2012.12235/) (2020) - [Iterative Machine Teaching](https://mlsanity.com/paper/1705.10470/) (2017) - [Near-Optimally Teaching the Crowd to Classify](https://mlsanity.com/paper/1402.2092/) (2014) - [Teaching Categories to Human Learners with Visual Explanations](https://mlsanity.com/paper/1802.06924/) (2018) - [When Pigs Fly: Contextual Reasoning in Synthetic and Natural Scenes](https://mlsanity.com/paper/2104.02215/) (2021) - [Do Perceptually Aligned Gradients Imply Adversarial Robustness?](https://mlsanity.com/paper/2207.11378/) (2022) - [The Best Defense is a Good Offense: Adversarial Augmentation against Adversarial Attacks](https://mlsanity.com/paper/2305.14188/) (2023) - [Becoming the Expert - Interactive Multi-Class Machine Teaching](https://mlsanity.com/paper/1504.07575/) (2015) - [Visual Search Asymmetry: Deep Nets and Humans Share Similar Inherent Biases](https://mlsanity.com/paper/2106.02953/) (2021) - [CLIPAG: Towards Generator-Free Text-to-Image Generation](https://mlsanity.com/paper/2306.16805/) (2023) - [A Dual-Stream Neural Network Explains the Functional Segregation of Dorsal and Ventral Visual Pathways in Human Brains](https://mlsanity.com/paper/2310.13849/) (2023) - [Iterative Teaching by Data Hallucination](https://mlsanity.com/paper/2210.17467/) (2022) - [Making a Bird AI Expert Work for You and Me](https://mlsanity.com/paper/2112.02747/) (2021) - [Amicable Aid: Perturbing Images to Improve Classification Performance](https://mlsanity.com/paper/2112.04720/) (2021) ## Built on by (most cited first, 2 of 2) - [Noninvasive precision modulation of high-level neural population activity via natural vision perturbations](https://mlsanity.com/paper/2506.05633/) (2025) - [Stretching Beyond the Obvious: A Gradient-Free Framework to Unveil the Hidden Landscape of Visual Invariance](https://mlsanity.com/paper/2506.17040/) (2025) ## Abstract (arXiv) The currently leading artificial neural network models of the visual ventral stream - which are derived from a combination of performance optimization and robustification methods - have demonstrated a remarkable degree of behavioral alignment with humans on visual categorization tasks. We show that image perturbations generated by these models can enhance the ability of humans to accurately report the ground truth class. Furthermore, we find that the same models can also be used out-of-the-box to predict the proportion of correct human responses to individual images, providing a simple, human-aligned estimator of the relative difficulty of each image. Motivated by these observations, we propose to augment visual learning in humans in a way that improves human categorization accuracy at test time. Our learning augmentation approach consists of (i) selecting images based on their model-estimated recognition difficulty, and (ii) applying image perturbations that aid recognition for novice learners. We find that combining these model-based strategies leads to categorization accuracy gains of 33-72% relative to control subjects without these interventions, on unmodified, randomly selected held-out test images. Beyond the accuracy gain, the training time for the augmented learning group was also shortened by 20-23%, despite both groups completing the same number of training trials. We demonstrate the efficacy of our approach in a fine-grained categorization task with natural images, as well as two tasks in clinically relevant image domains - histology and dermoscopy - where visual learning is notoriously challenging. To the best of our knowledge, our work is the first application of artificial neural networks to increase visual learning performance in humans by enhancing category-specific image features.