# Learning Loss for Test-Time Augmentation

arXiv: https://arxiv.org/abs/2010.11422 · HTML: https://mlsanity.com/paper/2010.11422/

## Key facts

- Cited by 56 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2020.

## Built on by (most cited first, 30 of 56)

- [A Survey of Uncertainty in Deep Neural Networks](https://mlsanity.com/paper/2107.03342/) (2021)
- [A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts](https://mlsanity.com/paper/2303.15361/) (2023)
- [Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Recognition](https://mlsanity.com/paper/2107.09249/) (2021)
- [LUQ: Long-text Uncertainty Quantification for LLMs](https://mlsanity.com/paper/2403.20279/) (2024)
- [VL-Uncertainty: Detecting Hallucination in Large Vision-Language Model via Uncertainty Estimation](https://mlsanity.com/paper/2411.11919/) (2024)
- [ImageNet-Hard: The Hardest Images Remaining from a Study of the Power of Zoom and Spatial Biases in Image Classification](https://mlsanity.com/paper/2304.05538/) (2023)
- [GraphPatcher: Mitigating Degree Bias for Graph Neural Networks via Test-time Augmentation](https://mlsanity.com/paper/2310.00800/) (2023)
- [Enhancing Adversarial Robustness via Test-time Transformation Ensembling](https://mlsanity.com/paper/2107.14110/) (2021)
- [Robustness via Cross-Domain Ensembles](https://mlsanity.com/paper/2103.10919/) (2021)
- [Understanding Test-Time Augmentation](https://mlsanity.com/paper/2402.06892/) (2024)
- [A Survey on the Robustness of Computer Vision Models against Common Corruptions](https://mlsanity.com/paper/2305.06024/) (2023)
- [MVDG: A Unified Multi-view Framework for Domain Generalization](https://mlsanity.com/paper/2112.12329/) (2021)
- [Fourier-basis Functions to Bridge Augmentation Gap: Rethinking Frequency Augmentation in Image Classification](https://mlsanity.com/paper/2403.01944/) (2024)
- [Learning Instance-Specific Augmentations by Capturing Local Invariances](https://mlsanity.com/paper/2206.00051/) (2022)
- [Test-Time Distribution Normalization for Contrastively Learned Vision-language Models](https://mlsanity.com/paper/2302.11084/) (2023)
- [Exploring Train and Test-Time Augmentations for Audio-Language Learning](https://mlsanity.com/paper/2210.17143/) (2022)
- [SwinFSR: Stereo Image Super-Resolution using SwinIR and Frequency Domain Knowledge](https://mlsanity.com/paper/2304.12556/) (2023)
- [Regularising for invariance to data augmentation improves supervised learning](https://mlsanity.com/paper/2203.03304/) (2022)
- [Online Continual Learning for Robust Indoor Object Recognition](https://mlsanity.com/paper/2307.09827/) (2023)
- [Improving Black-box Robustness with In-Context Rewriting](https://mlsanity.com/paper/2402.08225/) (2024)
- [Hierarchical Attention-based Age Estimation and Bias Estimation](https://mlsanity.com/paper/2103.09882/) (2021)
- [DC4L: Distribution Shift Recovery via Data-Driven Control for Deep Learning Models](https://mlsanity.com/paper/2302.10341/) (2023)
- [Approaching Test Time Augmentation in the Context of Uncertainty Calibration for Deep Neural Networks](https://mlsanity.com/paper/2304.05104/) (2023)
- [Mean Embeddings with Test-Time Data Augmentation for Ensembling of Representations](https://mlsanity.com/paper/2106.08038/) (2021)
- [How to Fine-tune Models with Few Samples: Update, Data Augmentation, and Test-time Augmentation](https://mlsanity.com/paper/2205.07874/) (2022)
- [Synergy with Translation Artifacts for Training and Inference in Multilingual Tasks](https://mlsanity.com/paper/2210.09588/) (2022)
- [Addressing Heterogeneity in Federated Learning via Distributional Transformation](https://mlsanity.com/paper/2210.15025/) (2022)
- [BayTTA: Uncertainty-aware medical image classification with optimized test-time augmentation using Bayesian model averaging](https://mlsanity.com/paper/2406.17640/) (2024)
- [Dynamic Test-Time Augmentation via Differentiable Functions](https://mlsanity.com/paper/2212.04681/) (2022)
- [Test-Time Augmentation for 3D Point Cloud Classification and Segmentation](https://mlsanity.com/paper/2311.13152/) (2023)

## Abstract (arXiv)

Data augmentation has been actively studied for robust neural networks. Most of the recent data augmentation methods focus on augmenting datasets during the training phase. At the testing phase, simple transformations are still widely used for test-time augmentation. This paper proposes a novel instance-level test-time augmentation that efficiently selects suitable transformations for a test input. Our proposed method involves an auxiliary module to predict the loss of each possible transformation given the input. Then, the transformations having lower predicted losses are applied to the input. The network obtains the results by averaging the prediction results of augmented inputs. Experimental results on several image classification benchmarks show that the proposed instance-aware test-time augmentation improves the model's robustness against various corruptions.