Can Machines Help Us Answering Question 16 in Datasheets, and In Turn Reflecting on Inappropriate Content?

Schramowski, Patrick; Tauchmann, Christopher; Kersting, Kristian

doi:10.1145/3531146.3533192

Cited by 9 publications

(23 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We apply Q16 [68] and our own specialized pornographic and sexualized content classifier (here referred to as NSFW) to identify and document a broad range of inappropriate concepts displaying not only persons but also objects, symbols, and text, see cf. [68] and Appendix Sec. C.5 and Sec.…”

Section: Safety During Collectionmentioning

confidence: 99%

“…Further, we used the Q16 documentation pipeline [68] to document the broad range of identified potentially inappropriate concepts contained, cf. Sec.…”

Section: C6 Further Inappropriate Content Taggingmentioning

confidence: 99%

“…Figure 5: Word cloud based on[68] documenting the potentially inappropriate image content of the LAION-5B subset which contains text in English language. Provided alternative text is used as text description of the images.…”

mentioning

confidence: 99%

See 2 more Smart Citations

LAION-5B: An open large-scale dataset for training next generation image-text models

Schuhmann¹,

Beaumont²,

Vencu³

et al. 2022

Preprint

View full text Add to dashboard Cite

Groundbreaking language-vision architectures like CLIP and DALL-E proved the utility of training on large amounts of noisy image-text data, without relying on expensive accurate labels used in standard vision unimodal supervised learning. The resulting models showed capabilities of strong text-guided image generation and transfer to downstream tasks, while performing remarkably at zero-shot classification with noteworthy out-of-distribution robustness. Since then, large-scale language-vision models like ALIGN, BASIC, GLIDE, Flamingo and Imagen made further improvements. Studying the training and capabilities of such models requires datasets containing billions of image-text pairs. Until now, no datasets of this size have been made openly available for the broader research community. To address this problem and democratize research on large-scale multi-modal models, we present LAION-5B -a dataset consisting of 5.85 billion CLIP-filtered image-text pairs, of which 2.32B contain English language. We show successful replication and fine-tuning of foundational models like CLIP, GLIDE and Stable Diffusion using the dataset, and discuss further experiments enabled with an openly available dataset of this scale. Additionally we provide several nearest neighbor indices, an improved web-interface for dataset exploration and subset generation, and detection scores for watermark, NSFW, and toxic content detection. 1 1 Project page: https://laion.ai/laion-5b-a-new-era-of-open-large-scale-multi-modal-datasets/

show abstract

Section: Safety During Collectionmentioning

confidence: 99%

“…Further, we used the Q16 documentation pipeline [68] to document the broad range of identified potentially inappropriate concepts contained, cf. Sec.…”

Section: C6 Further Inappropriate Content Taggingmentioning

confidence: 99%

See 1 more Smart Citation

LAION-5B: An open large-scale dataset for training next generation image-text models

Schuhmann¹,

Beaumont²,

Vencu³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…SD's post-hoc safety measures. Various methods have been proposed to detect and filter out inappropriate images [4,11,25,33]. Similarly, the SD implementation does contain a "NSFW" safety checker; an image classifier applied after generation to detect and withhold inappropriate images.…”

Section: Risks and Promises Of Unfiltered Datamentioning

confidence: 99%

“…Next to the prompts, our framework includes three dedicated inappropriateness detectors. Namely, SD's built-in safety checker, the Q16 classifier [33], and an explicit nudity classifier, the NudeNet mentioned earlier.…”

Section: Inappropriate Image Prompts (I2p)mentioning

confidence: 99%

Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models

Schramowski¹,

Brack²,

Deiseroth³

et al. 2022

Preprint

View full text Add to dashboard Cite

Text-conditioned image generation models have recently achieved astonishing results in image quality and text alignment and are consequently employed in a fast-growing number of applications. Since they are highly data-driven, relying on billion-sized datasets randomly scraped from the internet, they also suffer, as we demonstrate, from degenerated and biased human behavior. In turn, they may even reinforce such biases. To help combat these undesired side effects, we present safe latent diffusion (SLD). Specifically, to measure the inappropriate degeneration due to unfiltered and imbalanced training sets, we establish a novel image generation test bed-inappropriate image prompts (I2P)-containing dedicated, real-world image-totext prompts covering concepts such as nudity and violence. As our exhaustive empirical evaluation demonstrates, the introduced SLD removes and suppresses inappropriate image parts during the diffusion process, with no additional training required and no adverse effect on overall image quality or text alignment.

show abstract

Completeness of Datasets Documentation on ML/AI Repositories: An Empirical Investigation

Rondina,

Vetrò,

De Martin

2023

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Can Machines Help Us Answering Question 16 in Datasheets, and In Turn Reflecting on Inappropriate Content?

Cited by 9 publications

References 30 publications

LAION-5B: An open large-scale dataset for training next generation image-text models

LAION-5B: An open large-scale dataset for training next generation image-text models

Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models

Completeness of Datasets Documentation on ML/AI Repositories: An Empirical Investigation

Contact Info

Product

Resources

About