Built on

Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. pp. 234–241. Springer (2015)
2015
Earlier work this paper cites.
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML (2015)
2015
Earlier work this paper cites.
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: NeurIPS (2017)
2017
Earlier work this paper cites.
Van Den Oord, A., Vinyals, O., et al.: Neural discrete representation learning. In: NeurIPS (2017)
2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: NeurIPS (2017)
2017
Earlier work this paper cites.
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: NeurIPS (2020)
2020
Earlier work this paper cites.
Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., Poole, B.: Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456 (2020)
Original
2020
Earlier work this paper cites.
Birhane, A., Prabhu, V.U., Kahembwe, E.: Multimodal datasets: misogyny, pornography, and malignant stereotypes. arXiv preprint arXiv:2110.01963 (2021)
Original
2021
Earlier work this paper cites.
Changpinyo, S., Sharma, P., Ding, N., Soricut, R.: Conceptual 12M: Pushing web-scale image-text pre-training to recognize long-tail visual concepts. In: CVPR (2021)
2021
Earlier work this paper cites.
Chen, K., Hong, L., Xu, H., Li, Z., Yeung, D.Y.: Multisiam: Self-supervised multi-instance siamese representation learning for autonomous driving. In: ICCV (2021)
2021
Earlier work this paper cites.
Dhariwal, P., Nichol, A.: Diffusion models beat gans on image synthesis. In: NeurIPS (2021)
2021
Earlier work this paper cites.
Du, Y., Li, C., Guo, R., Cui, C., Liu, W., Zhou, J., Lu, B., Yang, Y., Liu, Q., Hu, X., et al.: Pp-ocrv2: Bag of tricks for ultra lightweight ocr system. arXiv preprint arXiv:2109.03144 (2021)
Original
2021
Earlier work this paper cites.
Han, J., Liang, X., Xu, H., Chen, K., Hong, L., Ye, C., Zhang, W., Li, Z., Liang, X., Xu, C.: Soda10m: Towards large-scale object detection benchmark for autonomous driving. arXiv preprint arXiv:2106.11118 (2021)
Original
2021
Earlier work this paper cites.
Kingma, D., Salimans, T., Poole, B., Ho, J.: Variational diffusion models. In: NeurIPS (2021)
2021
Earlier work this paper cites.
Schuhmann, C., Vencu, R., Beaumont, R., Kaczmarczyk, R., Mullis, C., Katta, A., Coombes, T., Jitsev, J., Komatsuzaki, A.: Laion-400m: Open dataset of clip-filtered 400 million image-text pairs. arXiv preprint arXiv:2111.02114 (2021)
Original
2021
Earlier work this paper cites.

Similar

Zhang, Y., Hooi, B., Hu, D., Liang, J., Feng, J.: Unleashing the power of contrastive self-supervised visual models via contrast-regularized fine-tuning. NeurIPS 34
2021
Cited alongside, same era.
Balaji, Y., Nah, S., Huang, X., Vahdat, A., Song, J., Kreis, K., Aittala, M., Aila, T., Laine, S., Catanzaro, B., et al.: ediffi: Text-to-image diffusion models with an ensemble of expert denoisers. arXiv preprint arXiv:2211.01324 (2022)
Original
2022
Cited alongside, same era.
Ho, J., Salimans, T.: Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598 (2022)
Original
2022
Cited alongside, same era.
Li, K., Chen, K., Wang, H., Hong, L., Ye, C., Han, J., Chen, Y., Zhang, W., Xu, C., Yeung, D.Y., et al.: Coda: A real-world road corner case dataset for object detection in autonomous driving. arXiv preprint arXiv:2203.07724 (2022)
Original
2022
Cited alongside, same era.

Then

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Reading the bibliography…

2023

Implicit Concept Removal of Diffusion Models

Liu, Zhili, Chen, Kai, Zhang, Yifan et al.

Understand

Text-to-image (T2I) diffusion models often inadvertently generate unwanted concepts such as watermarks and unsafe images.

These concepts, termed as the "implicit concepts", could be unintentionally learned during training and then be generated uncontrollably during inference.
Existing removal methods still struggle to eliminate implicit concepts primarily due to their dependency on the model's ability to recognize concepts it actually can not discern.
To address this, we utilize the intrinsic geometric characteristics of implicit concepts and present the Geom-Erasing, a novel concept removal method based on the geometric-driven control.

Built on

Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. pp. 234–241. Springer (2015)
2015
Earlier work this paper cites.
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML (2015)
2015
Earlier work this paper cites.
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: NeurIPS (2017)
2017
Earlier work this paper cites.
Van Den Oord, A., Vinyals, O., et al.: Neural discrete representation learning. In: NeurIPS (2017)
2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: NeurIPS (2017)
2017
Earlier work this paper cites.
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: NeurIPS (2020)
2020
Earlier work this paper cites.
Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., Poole, B.: Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456 (2020)
Original
2020
Earlier work this paper cites.
Birhane, A., Prabhu, V.U., Kahembwe, E.: Multimodal datasets: misogyny, pornography, and malignant stereotypes. arXiv preprint arXiv:2110.01963 (2021)
Original
2021
Earlier work this paper cites.
Changpinyo, S., Sharma, P., Ding, N., Soricut, R.: Conceptual 12M: Pushing web-scale image-text pre-training to recognize long-tail visual concepts. In: CVPR (2021)
2021
Earlier work this paper cites.
Chen, K., Hong, L., Xu, H., Li, Z., Yeung, D.Y.: Multisiam: Self-supervised multi-instance siamese representation learning for autonomous driving. In: ICCV (2021)
2021
Earlier work this paper cites.
Dhariwal, P., Nichol, A.: Diffusion models beat gans on image synthesis. In: NeurIPS (2021)
2021
Earlier work this paper cites.
Du, Y., Li, C., Guo, R., Cui, C., Liu, W., Zhou, J., Lu, B., Yang, Y., Liu, Q., Hu, X., et al.: Pp-ocrv2: Bag of tricks for ultra lightweight ocr system. arXiv preprint arXiv:2109.03144 (2021)
Original
2021
Earlier work this paper cites.
Han, J., Liang, X., Xu, H., Chen, K., Hong, L., Ye, C., Zhang, W., Li, Z., Liang, X., Xu, C.: Soda10m: Towards large-scale object detection benchmark for autonomous driving. arXiv preprint arXiv:2106.11118 (2021)
Original
2021
Earlier work this paper cites.
Kingma, D., Salimans, T., Poole, B., Ho, J.: Variational diffusion models. In: NeurIPS (2021)
2021
Earlier work this paper cites.
Schuhmann, C., Vencu, R., Beaumont, R., Kaczmarczyk, R., Mullis, C., Katta, A., Coombes, T., Jitsev, J., Komatsuzaki, A.: Laion-400m: Open dataset of clip-filtered 400 million image-text pairs. arXiv preprint arXiv:2111.02114 (2021)
Original
2021
Earlier work this paper cites.

Similar

Zhang, Y., Hooi, B., Hu, D., Liang, J., Feng, J.: Unleashing the power of contrastive self-supervised visual models via contrast-regularized fine-tuning. NeurIPS 34
2021
Cited alongside, same era.
Balaji, Y., Nah, S., Huang, X., Vahdat, A., Song, J., Kreis, K., Aittala, M., Aila, T., Laine, S., Catanzaro, B., et al.: ediffi: Text-to-image diffusion models with an ensemble of expert denoisers. arXiv preprint arXiv:2211.01324 (2022)
Original
2022
Cited alongside, same era.
Ho, J., Salimans, T.: Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598 (2022)
Original
2022
Cited alongside, same era.
Li, K., Chen, K., Wang, H., Hong, L., Ye, C., Han, J., Chen, Y., Zhang, W., Xu, C., Yeung, D.Y., et al.: Coda: A real-world road corner case dataset for object detection in autonomous driving. arXiv preprint arXiv:2203.07724 (2022)
Original
2022
Cited alongside, same era.

Then

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Liu, Z., Han, J., Chen, K., Hong, L., Xu, H., Xu, C., Li, Z.: Task-customized self-supervised pre-training with scalable dynamic routing. In: AAAI (2022)

2022

Cited alongside, same era.

Nichol, A.: Dall·e 2 pre-training mitigations. https://openai.com/research/dall-e-2-pre-training-mitigations (2022)

2022

Cited alongside, same era.

Pinkney, J.N.M.: Pokemon blip captions. https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions/ (2022)

2022

Cited alongside, same era.

Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: CVPR (2022)

2022

Cited alongside, same era.

Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E.L., Ghasemipour, K., Gontijo Lopes, R., Karagol Ayan, B., Salimans, T., et al.: Photorealistic text-to-image diffusion models with deep language understanding. In: NeurIPS (2022)

2022

Cited alongside, same era.

Schramowski, P., Tauchmann, C., Kersting, K.: Can machines help us answering question 16 in datasheets, and in turn reflecting on inappropriate content? In: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency. pp. 1350–1361 (2022)

2022

Cited alongside, same era.

Zhang, Y., Hooi, B., Hong, L., Feng, J.: Self-supervised aggregation of diverse experts for test-agnostic long-tailed recognition. NeurIPS 35

2022

Cited alongside, same era.

Brack, M., Friedrich, F., Hintersdorf, D., Struppek, L., Schramowski, P., Kersting, K.: Sega: Instructing diffusion using semantic dimensions. arXiv preprint arXiv:2301.12247 (2023)

Original

2023

Cited alongside, same era.

Kumari, N., Zhang, B., Wang, S.Y., Shechtman, E., Zhang, R., Zhu, J.Y.: Ablating concepts in text-to-image diffusion models. In: ICCV (2023)

2023

Closest in time.

Lee, T., Yasunaga, M., Meng, C., Mai, Y., Park, J.S., Gupta, A., Zhang, Y., Narayanan, D., Teufel, H.B., Bellagente, M., et al.: Holistic evaluation of text-to-image models. arXiv preprint arXiv:2311.04287 (2023)

Original

2023

Closest in time.

Li, P., Liu, Z., Chen, K., Hong, L., Zhuge, Y., Yeung, D.Y., Lu, H., Jia, X.: Trackdiffusion: Multi-object tracking data generation via diffusion models. arXiv preprint arXiv:2312.00651 (2023)

Original

2023

Closest in time.

Schramowski, P., Brack, M., Deiseroth, B., Kersting, K.: Safe latent diffusion: Mitigating inappropriate degeneration in diffusion models. In: CVPR (2023)

2023

Closest in time.

Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E., Lample, G.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023)

Original

2023

Closest in time.

Yang, Y., Gui, D., Yuan, Y., Ding, H., Hu, H., Chen, K.: Glyphcontrol: Glyph conditional control for visual text generation. arXiv preprint arXiv:2305.18259 (2023)

Original

2023

Closest in time.

Zhang, E., Wang, K., Xu, X., Wang, Z., Shi, H.: Forget-me-not: Learning to forget in text-to-image diffusion models. arXiv preprint arXiv:2211.08332 (2023)

Original