Improved Text Classification via Test-Time Augmentation
- URL: http://arxiv.org/abs/2206.13607v1
- Date: Mon, 27 Jun 2022 19:57:27 GMT
- Title: Improved Text Classification via Test-Time Augmentation
- Authors: Helen Lu, Divya Shanmugam, Harini Suresh, John Guttag
- Abstract summary: Test-time augmentation is an established technique to improve the performance of image classification models.
We present augmentation policies that yield significant accuracy improvements with language models.
Experiments across a binary classification task and dataset show that test-time augmentation can deliver consistent improvements.
- Score: 2.493374942115722
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Test-time augmentation -- the aggregation of predictions across transformed
examples of test inputs -- is an established technique to improve the
performance of image classification models. Importantly, TTA can be used to
improve model performance post-hoc, without additional training. Although
test-time augmentation (TTA) can be applied to any data modality, it has seen
limited adoption in NLP due in part to the difficulty of identifying
label-preserving transformations. In this paper, we present augmentation
policies that yield significant accuracy improvements with language models. A
key finding is that augmentation policy design -- for instance, the number of
samples generated from a single, non-deterministic augmentation -- has a
considerable impact on the benefit of TTA. Experiments across a binary
classification task and dataset show that test-time augmentation can deliver
consistent improvements over current state-of-the-art approaches.
Related papers
- Intelligent Multi-View Test Time Augmentation [14.11559987180237]
We introduce an intelligent Test Time Augmentation (TTA) algorithm designed to enhance the robustness and accuracy of image classification models against viewpoint variations.
Unlike traditional TTA methods that indiscriminately apply augmentations, our approach intelligently selects optimal augmentations based on predictive uncertainty metrics.
This research underscores the potential of adaptive, uncertainty-aware TTA in improving the robustness of image classification in the presence of viewpoint variations.
arXiv Detail & Related papers (2024-06-12T18:59:01Z) - Uncertainty-Calibrated Test-Time Model Adaptation without Forgetting [55.17761802332469]
Test-time adaptation (TTA) seeks to tackle potential distribution shifts between training and test data by adapting a given model w.r.t. any test sample.
Prior methods perform backpropagation for each test sample, resulting in unbearable optimization costs to many applications.
We propose an Efficient Anti-Forgetting Test-Time Adaptation (EATA) method which develops an active sample selection criterion to identify reliable and non-redundant samples.
arXiv Detail & Related papers (2024-03-18T05:49:45Z) - TEA: Test-time Energy Adaptation [67.4574269851666]
Test-time adaptation (TTA) aims to improve model generalizability when test data diverges from training distribution.
We propose a novel energy-based perspective, enhancing the model's perception of target data distributions.
arXiv Detail & Related papers (2023-11-24T10:49:49Z) - Improving Entropy-Based Test-Time Adaptation from a Clustering View [15.157208389691238]
We introduce a new clustering perspective on the entropy-based TTA.
We propose to improve EBTTA from the assignment step and the updating step, where robust label assignment, similarity-preserving constraint, sample selection, and gradient accumulation are proposed.
Experimental results demonstrate that our method can achieve consistent improvements on various datasets.
arXiv Detail & Related papers (2023-10-31T10:10:48Z) - Diverse Data Augmentation with Diffusions for Effective Test-time Prompt
Tuning [73.75282761503581]
We propose DiffTPT, which leverages pre-trained diffusion models to generate diverse and informative new data.
Our experiments on test datasets with distribution shifts and unseen categories demonstrate that DiffTPT improves the zero-shot accuracy by an average of 5.13%.
arXiv Detail & Related papers (2023-08-11T09:36:31Z) - RDumb: A simple approach that questions our progress in continual test-time adaptation [12.374649969346441]
Test-Time Adaptation (TTA) allows to update pre-trained models to changing data distributions at deployment time.
Recent work proposed and applied methods for continual adaptation over long timescales.
We find that eventually all but one state-of-the-art methods collapse and perform worse than a non-adapting model.
arXiv Detail & Related papers (2023-06-08T17:52:34Z) - A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts [143.14128737978342]
Test-time adaptation, an emerging paradigm, has the potential to adapt a pre-trained model to unlabeled data during testing, before making predictions.
Recent progress in this paradigm highlights the significant benefits of utilizing unlabeled data for training self-adapted models prior to inference.
arXiv Detail & Related papers (2023-03-27T16:32:21Z) - Efficient Test-Time Model Adaptation without Forgetting [60.36499845014649]
Test-time adaptation seeks to tackle potential distribution shifts between training and testing data.
We propose an active sample selection criterion to identify reliable and non-redundant samples.
We also introduce a Fisher regularizer to constrain important model parameters from drastic changes.
arXiv Detail & Related papers (2022-04-06T06:39:40Z) - Listen, Adapt, Better WER: Source-free Single-utterance Test-time
Adaptation for Automatic Speech Recognition [65.84978547406753]
Test-time Adaptation aims to adapt the model trained on source domains to yield better predictions for test samples.
Single-Utterance Test-time Adaptation (SUTA) is the first TTA study in speech area to our best knowledge.
arXiv Detail & Related papers (2022-03-27T06:38:39Z) - Learning Loss for Test-Time Augmentation [25.739449801033846]
This paper proposes a novel instance-level test-time augmentation that efficiently selects suitable transformations for a test input.
Experimental results on several image classification benchmarks show that the proposed instance-aware test-time augmentation improves the model's robustness against various corruptions.
arXiv Detail & Related papers (2020-10-22T03:56:34Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.