# A solution to generalized learning from small training sets found in infants repeated visual experiences of individual objects

arXiv: https://arxiv.org/abs/2510.15060 · HTML: https://mlsanity.com/paper/2510.15060/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 34 of its 144 references that are in the map.
- First posted on arXiv 16 Oct 2025.

## Builds on (most cited first, 30 of 34)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
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- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [Revisiting Unreasonable Effectiveness of Data in Deep Learning Era](https://mlsanity.com/paper/1707.02968/) (2017)
- [Measuring Robustness to Natural Distribution Shifts in Image Classification](https://mlsanity.com/paper/2007.00644/) (2020)
- [Explaining Neural Scaling Laws](https://mlsanity.com/paper/2102.06701/) (2021)
- [DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Data](https://mlsanity.com/paper/2306.09344/) (2023)
- [Perception Encoder: The best visual embeddings are not at the output of the network](https://mlsanity.com/paper/2504.13181/) (2025)
- [Data Distributional Properties Drive Emergent In-Context Learning in Transformers](https://mlsanity.com/paper/2205.05055/) (2022)
- [TinyViT: Fast Pretraining Distillation for Small Vision Transformers](https://mlsanity.com/paper/2207.10666/) (2022)
- [SCLIP: Rethinking Self-Attention for Dense Vision-Language Inference](https://mlsanity.com/paper/2312.01597/) (2023)
- [A Comprehensive Survey of Image Augmentation Techniques for Deep Learning](https://mlsanity.com/paper/2205.01491/) (2022)
- [The Effects of Regularization and Data Augmentation are Class Dependent](https://mlsanity.com/paper/2204.03632/) (2022)
- [Active Learning of Inverse Models with Intrinsically Motivated Goal Exploration in Robots](https://mlsanity.com/paper/1301.4862/) (2013)
- [Avoiding Overfitting: A Survey on Regularization Methods for Convolutional Neural Networks](https://mlsanity.com/paper/2201.03299/) (2022)
- [Understanding and Mitigating Numerical Sources of Nondeterminism in LLM Inference](https://mlsanity.com/paper/2506.09501/) (2025)
- [Nondeterminism and Instability in Neural Network Optimization](https://mlsanity.com/paper/2103.04514/) (2021)
- [On Model Stability as a Function of Random Seed](https://mlsanity.com/paper/1909.10447/) (2019)
- [The emergence of sparse attention: impact of data distribution and benefits of repetition](https://mlsanity.com/paper/2505.17863/) (2025)
- [Impacts of floating-point non-associativity on reproducibility for HPC and deep learning applications](https://mlsanity.com/paper/2408.05148/) (2024)
- [Amodal Completion and Size Constancy in Natural Scenes](https://mlsanity.com/paper/1509.08147/) (2015)
- [Image Amodal Completion: A Survey](https://mlsanity.com/paper/2207.02062/) (2022)
- [Deep Convolutional Neural Networks in the Face of Caricature: Identity and Image Revealed](https://mlsanity.com/paper/1812.10902/) (2018)

## Abstract (arXiv)

One-year-old infants rapidly form and generalize categories from idiosyncratic experiences of very few exemplars of those categories. Here we provide evidence on the statistics of infants daily-life visual experiences for 8 object categories. Using a corpus of infant head-camera images recorded at mealtimes (87 mealtimes,14 infants), we measure the frequency of the unique instances of each category and the variability of the visual experiences within and across instances of the same category. The frequency distributions of instances for individual infants are highly skewed, containing many images of the same few objects along with fewer images of other instances. Graph theoretic measures of individual category experiences for individual children reveal a lumpy mix of high similarity and high variability, organized into multiple but interconnected clusters of high-similarity images. In computational experiments, we show that artificially created training sets characterized by an interconnected mix of high and low similarity support generalization to novel instances after limited training. We discuss implications for category recognition, and for learning more generally, by both humans and machines.