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As a dominant force in text-to-image generation tasks, Diffusion Probabilistic Models (DPMs) face a critical challenge in controllability, struggling to adhere strictly to complex, multi-faceted instructions.
Sobolev Spaces , volume 140
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Stochastic first-and zeroth-order methods for nonconvex stochastic programming
Ghadimi, S. and Lan, G · 2013
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Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2014
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Inceptionism: Going deeper into neural networks, 2015
Alexander Mordvintsev, C. O. and Tyka, M · 2015
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Understanding deep image representations by inverting them
Mahendran, A. and Vedaldi, A · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
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Nonlinear Programming
Bertsekas, D · 2016
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Tarvainen, A. and Valpola, H · 2017
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Lectures on convex optimization , volume 137
Nesterov, Y. et al · 2018
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Memory efficient adaptive optimization
Anil, R., Gupta, V., Koren, T., and Singer, Y · 2019
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Certified adversarial robustness via randomized smoothing
Cohen, J., Rosenfeld, E., and Kolter, Z · 2019
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Stochastic gradient descent for nonconvex learning without bounded gradient assumptions
Lei, Y., Hu, T., Li, G., and Tang, K · 2019
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Certified adversarial robustness with additive noise
Li, B., Chen, C., Wang, W., and Carin, L · 2019
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Provably robust deep learning via adversarially trained smoothed classifiers
Salman, H., Li, J., Razenshteyn, I., Zhang, P., Zhang, H., Bubeck, S., and Yang, G · 2019
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Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai
Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., et al · 2020
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A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
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Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Hard negative mixing for contrastive learning
Kalantidis, Y., Sariyildiz, M. B., Pion, N., Weinzaepfel, P., and Larlus, D · 2020
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Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2020
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What makes for good views for contrastive learning?
Tian, Y., Sun, C., Poole, B., Krishnan, D., Schmid, C., and Isola, P · 2020
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Dreaming to distill: Data-free knowledge transfer via deepinversion
Yin, H., Molchanov, P., Alvarez, J. M., Li, Z., Mallya, A., Hoiem, D., Jha, N. K., and Kautz, J · 2020
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Exponential moving average normalization for self-supervised and semi-supervised learning
Cai, Z., Ravichandran, A., Maji, S., Fowlkes, C., Tu, Z., and Soatto, S · 2021
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Equivariant contrastive learning
Dangovski, R., Jing, L., Loh, C., Han, S., Srivastava, A., Cheung, B., Agrawal, P., and Soljačić, M · 2021
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Taming transformers for high-resolution image synthesis
Esser, P., Rombach, R., and Ommer, B · 2021
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Your contrastive learning is secretly doing stochastic neighbor embedding
Hu, T., Zhili, L., Zhou, F., Wang, W., and Huang, W · 2022
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The first optimal acceleration of high-order methods in smooth convex optimization
Kovalev, D. and Gasnikov, A · 2022
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Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation
Li, J., Li, D., Xiong, C., and Hoi, S · 2022
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Dreamfusion: Text-to-3d using 2d diffusion
Poole, B., Jain, A., Barron, J. T., and Mildenhall, B · 2022
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High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
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Feng, R., Lin, Z., Zhu, J., Zhao, D., Zhou, J., and Zha, Z.-J · 2021
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Clipscore: A reference-free evaluation metric for image captioning
Hessel, J., Holtzman, A., Forbes, M., Bras, R. L., and Choi, Y · 2021
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Scaling up visual and vision-language representation learning with noisy text supervision
Jia, C., Yang, Y., Xia, Y., Chen, Y.-T., Parekh, Z., Pham, H., Le, Q., Sung, Y.-H., Li, Z., and Duerig, T · 2021
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Align before fuse: Vision and language representation learning with momentum distillation
Li, J., Selvaraju, R., Gotmare, A., Joty, S., Xiong, C., and Hoi, S. C. H · 2021
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Glide: Towards photorealistic image generation and editing with text-guided diffusion models
Nichol, A., Dhariwal, P., Ramesh, A., Shyam, P., Mishkin, P., McGrew, B., Sutskever, I., and Chen, M · 2021
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On compositions of transformations in contrastive self-supervised learning
Patrick, M., Asano, Y. M., Kuznetsova, P., Fong, R., Henriques, J. F., Zweig, G., and Vedaldi, A · 2021
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Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
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Wang, G. and Torr, P. H · 2022
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Universal guidance for diffusion models
Bansal, A., Chu, H.-M., Schwarzschild, A., Sengupta, S., Goldblum, M., Geiping, J., and Goldstein, T · 2023
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Improving image generation with better captions
Betker, J., Goh, G., Jing, L., Brooks, T., Wang, J., Li, L., Ouyang, L., Zhuang, J., Lee, J., Guo, Y., et al · 2023
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Attend-and-excite: Attention-based semantic guidance for text-to-image diffusion models
Chefer, H., Alaluf, Y., Vinker, Y., Wolf, L., and Cohen-Or, D · 2023
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Random smoothing regularization in kernel gradient descent learning
Ding, L., Hu, T., Jiang, J., Li, D., Wang, W., and Yao, Y · 2023
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Delta denoising score
Hertz, A., Aberman, K., and Cohen-Or, D · 2023
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Complexity matters: Rethinking the latent space for generative modeling
Hu, T., Chen, F., Wang, H., Li, J., Wang, W., Sun, J., and Li, Z · 2023
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T2i-compbench: A comprehensive benchmark for open-world compositional text-to-image generation
Huang, K., Sun, K., Xie, E., Li, Z., and Liu, X · 2023
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Lian, L., Li, B., Yala, A., and Darrell, T · 2023
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Diff-instruct: A universal approach for transferring knowledge from pre-trained diffusion models
Luo, W., Hu, T., Zhang, S., Sun, J., Li, Z., and Zhang, Z · 2023
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Elucidating the design space of classifier-guided diffusion generation
Ma, J., Hu, T., Wang, W., and Sun, J · 2023
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Dalle-2, 2023
OpenAI · 2023
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Grounded text-to-image synthesis with attention refocusing
Phung, Q., Ge, S., and Huang, J.-B · 2023
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Sdxl: Improving latent diffusion models for high-resolution image synthesis
Podell, D., English, Z., Lacey, K., Blattmann, A., Dockhorn, T., Müller, J., Penna, J., and Rombach, R · 2023
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Fine-tuning language models with just forward passes
Malladi, S., Gao, T., Nichani, E., Damian, A., Lee, J. D., Chen, D., and Arora, S · 2024
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