Diverse Data Augmentation with Diffusions for Effective Test-time Prompt Tuning Article Swipe
Chun-Mei Feng
,
Kai Yu
,
Yong Liu
,
Salman Khan
,
Wangmeng Zuo
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2308.06038
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2308.06038
Benefiting from prompt tuning, recent years have witnessed the promising performance of pre-trained vision-language models, e.g., CLIP, on versatile downstream tasks. In this paper, we focus on a particular setting of learning adaptive prompts on the fly for each test sample from an unseen new domain, which is known as test-time prompt tuning (TPT). Existing TPT methods typically rely on data augmentation and confidence selection. However, conventional data augmentation techniques, e.g., random resized crops, suffers from the lack of data diversity, while entropy-based confidence selection alone is not sufficient to guarantee prediction fidelity. To address these issues, we propose a novel TPT method, named DiffTPT, which leverages pre-trained diffusion models to generate diverse and informative new data. Specifically, we incorporate augmented data by both conventional method and pre-trained stable diffusion to exploit their respective merits, improving the models ability to adapt to unknown new test data. Moreover, to ensure the prediction fidelity of generated data, we introduce a cosine similarity-based filtration technique to select the generated data with higher similarity to the single test sample. 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\% compared to the state-of-the-art TPT method. Our code and models will be publicly released.
Related Topics
Concepts
Computer science
Fidelity
Cosine similarity
Machine learning
Exploit
Artificial intelligence
Entropy (arrow of time)
Test data
Selection (genetic algorithm)
Similarity (geometry)
Data mining
Pattern recognition (psychology)
Programming language
Telecommunications
Quantum mechanics
Image (mathematics)
Computer security
Physics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2308.06038
- https://arxiv.org/pdf/2308.06038
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385825520
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385825520Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2308.06038Digital Object Identifier
- Title
-
Diverse Data Augmentation with Diffusions for Effective Test-time Prompt TuningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-08-11Full publication date if available
- Authors
-
Chun-Mei Feng, Kai Yu, Yong Liu, Salman Khan, Wangmeng ZuoList of authors in order
- Landing page
-
https://arxiv.org/abs/2308.06038Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2308.06038Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2308.06038Direct OA link when available
- Concepts
-
Computer science, Fidelity, Cosine similarity, Machine learning, Exploit, Artificial intelligence, Entropy (arrow of time), Test data, Selection (genetic algorithm), Similarity (geometry), Data mining, Pattern recognition (psychology), Programming language, Telecommunications, Quantum mechanics, Image (mathematics), Computer security, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.tasks. | 20 |
| abstract_inverted_index.tuning | 52 |
| abstract_inverted_index.unseen | 43, 184 |
| abstract_inverted_index.DiffTPT | 188 |
| abstract_inverted_index.ability | 138 |
| abstract_inverted_index.address | 94 |
| abstract_inverted_index.average | 195 |
| abstract_inverted_index.diverse | 112 |
| abstract_inverted_index.domain, | 45 |
| abstract_inverted_index.exploit | 131 |
| abstract_inverted_index.issues, | 96 |
| abstract_inverted_index.merits, | 134 |
| abstract_inverted_index.method, | 102 |
| abstract_inverted_index.method. | 203 |
| abstract_inverted_index.methods | 56 |
| abstract_inverted_index.models, | 14 |
| abstract_inverted_index.prompts | 33 |
| abstract_inverted_index.propose | 98 |
| abstract_inverted_index.resized | 72 |
| abstract_inverted_index.sample. | 174 |
| abstract_inverted_index.setting | 29 |
| abstract_inverted_index.suffers | 74 |
| abstract_inverted_index.tuning, | 3 |
| abstract_inverted_index.unknown | 142 |
| abstract_inverted_index.DiffTPT, | 104 |
| abstract_inverted_index.Existing | 54 |
| abstract_inverted_index.However, | 65 |
| abstract_inverted_index.accuracy | 192 |
| abstract_inverted_index.adaptive | 32 |
| abstract_inverted_index.compared | 198 |
| abstract_inverted_index.datasets | 179 |
| abstract_inverted_index.fidelity | 151 |
| abstract_inverted_index.generate | 111 |
| abstract_inverted_index.improves | 189 |
| abstract_inverted_index.learning | 31 |
| abstract_inverted_index.publicly | 210 |
| abstract_inverted_index.Moreover, | 146 |
| abstract_inverted_index.augmented | 120 |
| abstract_inverted_index.diffusion | 108, 129 |
| abstract_inverted_index.fidelity. | 92 |
| abstract_inverted_index.generated | 153, 165 |
| abstract_inverted_index.guarantee | 90 |
| abstract_inverted_index.improving | 135 |
| abstract_inverted_index.introduce | 156 |
| abstract_inverted_index.leverages | 106 |
| abstract_inverted_index.promising | 9 |
| abstract_inverted_index.released. | 211 |
| abstract_inverted_index.selection | 84 |
| abstract_inverted_index.technique | 161 |
| abstract_inverted_index.test-time | 50 |
| abstract_inverted_index.typically | 57 |
| abstract_inverted_index.versatile | 18 |
| abstract_inverted_index.witnessed | 7 |
| abstract_inverted_index.zero-shot | 191 |
| abstract_inverted_index.Benefiting | 0 |
| abstract_inverted_index.categories | 185 |
| abstract_inverted_index.confidence | 63, 83 |
| abstract_inverted_index.diversity, | 80 |
| abstract_inverted_index.downstream | 19 |
| abstract_inverted_index.filtration | 160 |
| abstract_inverted_index.particular | 28 |
| abstract_inverted_index.prediction | 91, 150 |
| abstract_inverted_index.respective | 133 |
| abstract_inverted_index.selection. | 64 |
| abstract_inverted_index.similarity | 169 |
| abstract_inverted_index.sufficient | 88 |
| abstract_inverted_index.demonstrate | 186 |
| abstract_inverted_index.experiments | 176 |
| abstract_inverted_index.incorporate | 119 |
| abstract_inverted_index.informative | 114 |
| abstract_inverted_index.performance | 10 |
| abstract_inverted_index.pre-trained | 12, 107, 127 |
| abstract_inverted_index.techniques, | 69 |
| abstract_inverted_index.augmentation | 61, 68 |
| abstract_inverted_index.conventional | 66, 124 |
| abstract_inverted_index.distribution | 181 |
| abstract_inverted_index.Specifically, | 117 |
| abstract_inverted_index.entropy-based | 82 |
| abstract_inverted_index.vision-language | 13 |
| abstract_inverted_index.similarity-based | 159 |
| abstract_inverted_index.state-of-the-art | 201 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |