# Viewmaker Networks: Learning Views for Unsupervised Representation Learning

arXiv: https://arxiv.org/abs/2010.07432 · HTML: https://mlsanity.com/paper/2010.07432/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2020.

## Built on by (most cited first, 30 of 48)

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- [Equivariant Contrastive Learning](https://mlsanity.com/paper/2111.00899/) (2021)
- [Advances and Challenges in Meta-Learning: A Technical Review](https://mlsanity.com/paper/2307.04722/) (2023)
- [MedAug: Contrastive learning leveraging patient metadata improves representations for chest X-ray interpretation](https://mlsanity.com/paper/2102.10663/) (2021)
- [Implicit Sample Extension for Unsupervised Person Re-Identification](https://mlsanity.com/paper/2204.06892/) (2022)
- [L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated Self-Supervised Visual Representation Learning](https://mlsanity.com/paper/2307.07393/) (2023)
- [Temperature as Uncertainty in Contrastive Learning](https://mlsanity.com/paper/2110.04403/) (2021)
- [Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices](https://mlsanity.com/paper/2111.03220/) (2021)
- [Not All Semantics are Created Equal: Contrastive Self-supervised Learning with Automatic Temperature Individualization](https://mlsanity.com/paper/2305.11965/) (2023)
- [Utilizing Expert Features for Contrastive Learning of Time-Series Representations](https://mlsanity.com/paper/2206.11517/) (2022)
- [Learning Instance-Specific Augmentations by Capturing Local Invariances](https://mlsanity.com/paper/2206.00051/) (2022)
- [Learning Representations with Contrastive Self-Supervised Learning for Histopathology Applications](https://mlsanity.com/paper/2112.05760/) (2021)
- [Detecting Anomalies within Time Series using Local Neural Transformations](https://mlsanity.com/paper/2202.03944/) (2022)
- [Parametric Augmentation for Time Series Contrastive Learning](https://mlsanity.com/paper/2402.10434/) (2024)
- [Timers and Such: A Practical Benchmark for Spoken Language Understanding with Numbers](https://mlsanity.com/paper/2104.01604/) (2021)
- [Ignorance is Bliss: Robust Control via Information Gating](https://mlsanity.com/paper/2303.06121/) (2023)
- [Feature Dropout: Revisiting the Role of Augmentations in Contrastive Learning](https://mlsanity.com/paper/2212.08378/) (2022)
- [GenView: Enhancing View Quality with Pretrained Generative Model for Self-Supervised Learning](https://mlsanity.com/paper/2403.12003/) (2024)
- [Characterizing and Improving the Robustness of Self-Supervised Learning through Background Augmentations](https://mlsanity.com/paper/2103.12719/) (2021)
- [Hybrid Generative-Contrastive Representation Learning](https://mlsanity.com/paper/2106.06162/) (2021)
- [SelfVC: Voice Conversion With Iterative Refinement using Self Transformations](https://mlsanity.com/paper/2310.09653/) (2023)
- [Modality-Agnostic Self-Supervised Learning with Meta-Learned Masked Auto-Encoder](https://mlsanity.com/paper/2310.16318/) (2023)
- [GCL: Gradient-Guided Contrastive Learning for Medical Image Segmentation with Multi-Perspective Meta Labels](https://mlsanity.com/paper/2309.08888/) (2023)
- [Empirical Evaluation of Data Augmentations for Biobehavioral Time Series Data with Deep Learning](https://mlsanity.com/paper/2210.06701/) (2022)
- [Constructive Assimilation: Boosting Contrastive Learning Performance through View Generation Strategies](https://mlsanity.com/paper/2304.00601/) (2023)
- [Enhancing Anomaly Detection via Generating Diversified and Hard-to-distinguish Synthetic Anomalies](https://mlsanity.com/paper/2409.10069/) (2024)
- [Improving Compositionality of Neural Networks by Decoding Representations to Inputs](https://mlsanity.com/paper/2106.00769/) (2021)

## Abstract (arXiv)

Many recent methods for unsupervised representation learning train models to be invariant to different "views," or distorted versions of an input. However, designing these views requires considerable trial and error by human experts, hindering widespread adoption of unsupervised representation learning methods across domains and modalities. To address this, we propose viewmaker networks: generative models that learn to produce useful views from a given input. Viewmakers are stochastic bounded adversaries: they produce views by generating and then adding an ℓp-bounded perturbation to the input, and are trained adversarially with respect to the main encoder network. Remarkably, when pretraining on CIFAR-10, our learned views enable comparable transfer accuracy to the well-tuned SimCLR augmentations -- despite not including transformations like cropping or color jitter. Furthermore, our learned views significantly outperform baseline augmentations on speech recordings (+9% points, on average) and wearable sensor data (+17% points). Viewmakers can also be combined with handcrafted views: they improve robustness to common image corruptions and can increase transfer performance in cases where handcrafted views are less explored. These results suggest that viewmakers may provide a path towards more general representation learning algorithms -- reducing the domain expertise and effort needed to pretrain on a much wider set of domains. Code is available at https://github.com/alextamkin/viewmaker.