BGDB: Bernoulli-Gaussian Decision Block with Improved Denoising Diffusion Probabilistic Models Article Swipe
Chengkun Sun
,
Jinqian Pan
,
Robert Terry
,
Jiang Bian
,
Jie Xu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v39i19.34279
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v39i19.34279
Generative models can enhance discriminative classifiers by constructing complex feature spaces, thereby improving performance on intricate datasets. Conventional methods typically augment datasets with more detailed feature representations or increase dimensionality to make nonlinear data linearly separable. Utilizing a generative model solely for feature space processing falls short of unlocking its full potential within a classifier and typically lacks a solid theoretical foundation. We base our approach on a novel hypothesis: the probability information (logit) derived from a single model training can be used to generate the equivalent of multiple training sessions. Leveraging the central limit theorem, this synthesized probability information is anticipated to converge toward the true probability more accurately. To achieve this goal, we propose the Bernoulli-Gaussian Decision Block (BGDB), a novel module inspired by the Central Limit Theorem and the concept that the mean of multiple Bernoulli trials approximates the probability of success in a single trial. Specifically, we utilize Improved Denoising Diffusion Probabilistic Models (IDDPM) to model the probability of Bernoulli Trials. Our approach shifts the focus from reconstructing features to reconstructing logits, transforming the logit from a single iteration into logits analogous to those from multiple experiments. We provide the theoretical foundations of our approach through mathematical analysis and validate its effectiveness through experimental evaluation using various datasets for multiple imaging tasks, including both classification and segmentation.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1609/aaai.v39i19.34279
- https://ojs.aaai.org/index.php/AAAI/article/download/34279/36434
- OA Status
- diamond
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409363925
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4409363925Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1609/aaai.v39i19.34279Digital Object Identifier
- Title
-
BGDB: Bernoulli-Gaussian Decision Block with Improved Denoising Diffusion Probabilistic ModelsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-04-11Full publication date if available
- Authors
-
Chengkun Sun, Jinqian Pan, Robert Terry, Jiang Bian, Jie XuList of authors in order
- Landing page
-
https://doi.org/10.1609/aaai.v39i19.34279Publisher landing page
- PDF URL
-
https://ojs.aaai.org/index.php/AAAI/article/download/34279/36434Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://ojs.aaai.org/index.php/AAAI/article/download/34279/36434Direct OA link when available
- Concepts
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Bernoulli's principle, Probabilistic logic, Diffusion, Gaussian, Block (permutation group theory), Computer science, Noise reduction, Mathematics, Artificial intelligence, Physics, Combinatorics, Thermodynamics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.equivalent | 86 |
| abstract_inverted_index.evaluation | 208 |
| abstract_inverted_index.generative | 38 |
| abstract_inverted_index.processing | 44 |
| abstract_inverted_index.separable. | 35 |
| abstract_inverted_index.accurately. | 109 |
| abstract_inverted_index.anticipated | 101 |
| abstract_inverted_index.classifiers | 5 |
| abstract_inverted_index.foundation. | 61 |
| abstract_inverted_index.foundations | 195 |
| abstract_inverted_index.hypothesis: | 69 |
| abstract_inverted_index.information | 72, 99 |
| abstract_inverted_index.performance | 13 |
| abstract_inverted_index.probability | 71, 98, 107, 142, 161 |
| abstract_inverted_index.synthesized | 97 |
| abstract_inverted_index.theoretical | 60, 194 |
| abstract_inverted_index.Conventional | 17 |
| abstract_inverted_index.approximates | 140 |
| abstract_inverted_index.constructing | 7 |
| abstract_inverted_index.experimental | 207 |
| abstract_inverted_index.experiments. | 190 |
| abstract_inverted_index.mathematical | 200 |
| abstract_inverted_index.transforming | 176 |
| abstract_inverted_index.Probabilistic | 155 |
| abstract_inverted_index.Specifically, | 149 |
| abstract_inverted_index.effectiveness | 205 |
| abstract_inverted_index.segmentation. | 220 |
| abstract_inverted_index.classification | 218 |
| abstract_inverted_index.dimensionality | 29 |
| abstract_inverted_index.discriminative | 4 |
| abstract_inverted_index.reconstructing | 171, 174 |
| abstract_inverted_index.representations | 26 |
| abstract_inverted_index.Bernoulli-Gaussian | 117 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.25477707 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |