Browse State-of-the-Art › Diversity
Diversity
3,166 papers with code · 0 benchmarks · 0 datasets archive 2025-07-28
Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.
Description from the archive archive 2025-07-28.
Benchmarks archive 2025-07-28
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Datasets archive 2025-07-28
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Most implemented papers archive 2025-07-28
30 shown of 3,166 papers with code (9,051 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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23 Oct 2019 57 repositories listed Syntology ran 2 of 31 samples · 29 unverifiedTransfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP).
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28 Mar 2016 39 repositories listed Syntology ran 32 of 73 samples · 41 unverified · 41 pointer-only (licence)We embrace the underlying uncertainty of the problem by posing it as a classification task and use class-rebalancing at training time to increase the diversity of colors in the result.
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30 Oct 2016 37 repositories listed Syntology ran 5 of 5 samples · 0 unverified · 5 pointer-only (licence)We expand on previous work for image quality assessment to provide two new analyses for assessing the discriminability and diversity of samples from class-conditional image synthesis models.
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7 Oct 2016 25 repositories listed Syntology ran 7 of 14 samples · 7 unverified · 14 pointer-only (licence)We observe that our method consistently outperforms BS and previously proposed techniques for diverse decoding from neural sequence models.
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31 Dec 2020 22 repositories listed Syntology ran 1 of 1 samples · 0 unverifiedRecent work has demonstrated that increased training dataset diversity improves general cross-domain knowledge and downstream generalization capability for large-scale language models.
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11 May 2021 21 repositories listed Syntology ran 27 of 50 samples · 23 unverified · 16 pointer-only (licence)Finally, we find that classifier guidance combines well with upsampling diffusion models, further improving FID to 3.
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31 Mar 2017 18 repositories listedWe propose a new equilibrium enforcing method paired with a loss derived from the Wasserstein distance for training auto-encoder based Generative Adversarial Networks.
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22 Apr 2019 17 repositories listed Syntology ran 8 of 17 samples · 9 unverified · 11 pointer-only (licence)Despite considerable advancements with deep neural language models, the enigma of neural text degeneration persists when these models are tested as text generators.
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2 Jun 2019 15 repositories listed Syntology ran 2 of 9 samples · 7 unverified · 4 pointer-only (licence)We explore the use of Vector Quantized Variational AutoEncoder (VQ-VAE) models for large scale image generation.
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4 Dec 2019 14 repositories listed Syntology ran 0 of 5 samples · 5 unverifiedA good image-to-image translation model should learn a mapping between different visual domains while satisfying the following properties: 1) diversity of generated images and 2) scalability over multiple domains.
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11 Oct 2015 14 repositories listed Syntology ran 2 of 13 samples · 11 unverifiedSequence-to-sequence neural network models for generation of conversational responses tend to generate safe, commonplace responses (e.
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28 Mar 2018 13 repositories listed Syntology ran 0 of 4 samples · 4 unverifiedTraining of neural networks for automated diagnosis of pigmented skin lesions is hampered by the small size and lack of diversity of available datasets of dermatoscopic images.
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26 Jul 2022 11 repositories listed Syntology ran 21 of 30 samples · 9 unverified · 4 pointer-only (licence)Classifier guidance is a recently introduced method to trade off mode coverage and sample fidelity in conditional diffusion models post training, in the same spirit as low temperature sampling or truncation in other…
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21 Sep 2020 11 repositories listed Syntology ran 20 of 33 samples · 13 unverifiedIn this work, we propose DiffWave, a versatile diffusion probabilistic model for conditional and unconditional waveform generation.
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24 Oct 2016 11 repositories listedIn this work we investigate the construction of a single, scalable deep network that can parsimoniously capture the artistic style of a diversity of paintings.
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28 Nov 2017 10 repositories listed Syntology ran 0 of 3 samples · 3 unverified · 3 pointer-only (licence)In this paper, we explicitly consider this challenge by introducing camera style (CamStyle) adaptation.
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10 Dec 2019 9 repositories listed Syntology ran 0 of 10 samples · 10 unverifiedIn an effort to help align the research community's contributions with real-world self-driving problems, we introduce a new large scale, high quality, diverse dataset.
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18 Oct 2018 9 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)Sequence-to-sequence models have recently gained the state of the art performance in summarization.
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7 Nov 2016 9 repositories listedWe introduce a method to stabilize Generative Adversarial Networks (GANs) by defining the generator objective with respect to an unrolled optimization of the discriminator.
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13 Apr 2022 8 repositories listed Syntology ran 29 of 38 samples · 9 unverified · 1 pointer-only (licence)Contrastive models like CLIP have been shown to learn robust representations of images that capture both semantics and style.
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25 Jun 2020 8 repositories listed Syntology ran 2 of 3 samples · 1 unverified · 3 pointer-only (licence)SRFlow therefore directly accounts for the ill-posed nature of the problem, and learns to predict diverse photo-realistic high-resolution images.
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5 Jun 2016 8 repositories listedRecent neural models of dialogue generation offer great promise for generating responses for conversational agents, but tend to be shortsighted, predicting utterances one at a time while ignoring their influence on…
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27 Apr 2021 7 repositories listed Syntology ran 1 of 1 samples · 0 unverifiedAs a result, our network can effectively improve the visual quality of underwater images by exploiting multiple color spaces embedding and the advantages of both physical model-based and learning-based methods.
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21 Oct 2019 7 repositories listed Syntology ran 6 of 21 samples · 15 unverifiedAudio captioning is the novel task of general audio content description using free text.
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23 Apr 2019 7 repositories listed Syntology ran 5 of 6 samples · 1 unverifiedTransformers are powerful sequence models, but require time and memory that grows quadratically with the sequence length.
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2 Aug 2018 7 repositories listedOur model takes the encoded content features extracted from a given input and the attribute vectors sampled from the attribute space to produce diverse outputs at test time.
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12 Dec 2017 7 repositories listedGenerative adversarial networks (GANs) are innovative techniques for learning generative models of complex data distributions from samples.
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30 Nov 2017 7 repositories listed Syntology ran 0 of 1 samples · 1 unverified · 1 pointer-only (licence)Our proposed method encourages bijective consistency between the latent encoding and output modes.
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1 Aug 2017 7 repositories listed Syntology ran 1 of 1 samples · 0 unverifiedNLP tasks are often limited by scarcity of manually annotated data.
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1 Dec 2022 6 repositories listed Syntology ran 10 of 14 samples · 4 unverifiedWe present Fast Language-Image Pre-training (FLIP), a simple and more efficient method for training CLIP.
Syntology lines on 24 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
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