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Recent works on personalized text-to-image generation usually learn to bind a special token with specific subjects or styles of a few given images by tuning its embedding through gradient descent.
Evolution strategy: Nature’s way of optimization
Rechenberg, I · 1989
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Completely derandomized self-adaptation in evolution strategies
Hansen, N. and Ostermeier, A · 2001
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Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (cma-es)
Hansen, N., Müller, S. D., and Koumoutsakos, P · 2003
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Optimization by direct search: New perspectives on some classical and modern methods
Kolda, T. G., Lewis, R. M., and Torczon, V · 2003
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Introduction to derivative-free optimization
Conn, A. R., Scheinberg, K., and Vicente, L. N · 2009
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Practical bayesian optimization of machine learning algorithms
Snoek, J., Larochelle, H., and Adams, R. P · 2012
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Derivative-free optimization: a review of algorithms and comparison of software implementations
Rios, L. M. and Sahinidis, N. V · 2013
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A tutorial on principal component analysis
Shlens, J · 2014
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Improving object detection with deep convolutional networks via bayesian optimization and structured prediction
Zhang, Y., Sohn, K., Villegas, R., Pan, G., and Lee, H · 2015
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The cma evolution strategy: A tutorial
Hansen, N · 2016
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Derivative-free optimization of high-dimensional non-convex functions by sequential random embeddings
Qian, H., Hu, Y.-Q., and Yu, Y · 2016
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An overview of gradient descent optimization algorithms
Ruder, S · 2016
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Bayesian optimization in a billion dimensions via random embeddings
Wang, Z., Hutter, F., Zoghi, M., Matheson, D., and De Feitas, N · 2016
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Sequential classification-based optimization for direct policy search
Hu, Y.-Q., Qian, H., and Yu, Y · 2017
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Evolution strategies as a scalable alternative to reinforcement learning
Salimans, T., Ho, J., Chen, X., Sidor, S., and Sutskever, I · 2017
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A tutorial on bayesian optimization
Frazier, P. I · 2018
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Stochastic zeroth-order optimization in high dimensions
Wang, Y., Du, S., Balakrishnan, S., and Singh, A · 2018
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Photographic text-to-image synthesis with a hierarchically-nested adversarial network
Zhang, Z., Xie, Y., and Yang, L · 2018
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Genattack: Practical black-box attacks with gradient-free optimization
Alzantot, M., Sharma, Y., Chakraborty, S., Zhang, H., Hsieh, C.-J., and Srivastava, M. B · 2019
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Towards gradient free and projection free stochastic optimization
Sahu, A. K., Zaheer, M., and Kar, S · 2019
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Personalized explanation in machine learning: A conceptualization
Schneider, J. and Handali, J · 2019
Cited alongside, same era.
Semantic object accuracy for generative text-to-image synthesis
Hinz, T., Heinrich, S., and Wermter, S · 2020
Cited alongside, same era.
Re-examining linear embeddings for high-dimensional bayesian optimization
Letham, B., Calandra, R., Rai, A., and Bakshy, E · 2020
Cited alongside, same era.
Denoising diffusion implicit models
Song, J., Meng, C., and Ermon, S · 2020
Cited alongside, same era.
Df-gan: Deep fusion generative adversarial networks for text-to-image synthesis
Tao, M., Tang, H., Wu, S., Sebe, N., Jing, X.-Y., Wu, F., and Bao, B · 2020
Cited alongside, same era.
Diffedit: Diffusion-based semantic image editing with mask guidance
Couairon, G., Verbeek, J., Schwenk, H., and Cord, M · 2022
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Vqgan-clip: Open domain image generation and editing with natural language guidance
Crowson, K., Biderman, S., Kornis, D., Stander, D., Hallahan, E., Castricato, L., and Raff, E · 2022
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Multiresolution textual inversion
Daras, G. and Dimakis, A. G · 2022
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Cogview2: Faster and better text-to-image generation via hierarchical transformers
Ding, M., Zheng, W., Hong, W., and Tang, J · 2022
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Make-a-scene: Scene-based text-to-image generation with human priors
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Transformers: State-of-the-art natural language processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., et al · 2020
Cited alongside, same era.
Search personalization using machine learning
Yoganarasimhan, H · 2020
Cited alongside, same era.
Intrinsic dimensionality explains the effectiveness of language model fine-tuning
Aghajanyan, A., Gupta, S., and Zettlemoyer, L · 2021
Cited alongside, same era.
Instance-conditioned gan
Casanova, A., Careil, M., Verbeek, J., Drozdzal, M., and Romero Soriano, A · 2021
Cited alongside, same era.
Ilvr: Conditioning method for denoising diffusion probabilistic models
Choi, J., Kim, S., Jeong, Y., Gwon, Y., and Yoon, S · 2021
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Dhariwal, P. and Nichol, A · 2021
Cited alongside, same era.
Cogview: Mastering text-to-image generation via transformers
Ding, M., Yang, Z., Hong, W., Zheng, W., Zhou, C., Yin, D., Lin, J., Zou, X., Shao, Z., Yang, H., et al · 2021
Cited alongside, same era.
Gafni, O., Polyak, A., Ashual, O., Sheynin, S., Parikh, D., and Taigman, Y · 2022
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An image is worth one word: Personalizing text-to-image generation using textual inversion
Gal, R., Alaluf, Y., Atzmon, Y., Patashnik, O., Bermano, A. H., Chechik, G., and Cohen-Or, D · 2022
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Prompt-to-prompt image editing with cross attention control
Hertz, A., Mokady, R., Tenenbaum, J., Aberman, K., Pritch, Y., and Cohen-Or, D · 2022
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Face generation and editing with stylegan: A survey
Melnik, A., Miasayedzenkau, M., Makarovets, D., Pirshtuk, D., Akbulut, E., Holzmann, D., Renusch, T., Reichert, G., and Ritter, H · 2022
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Null-text inversion for editing real images using guided diffusion models
Mokady, R., Hertz, A., Aberman, K., Pritch, Y., and Cohen-Or, D · 2022
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Mystyle: A personalized generative prior
Nitzan, Y., Aberman, K., He, Q., Liba, O., Yarom, M., Gandelsman, Y., Mosseri, I., Pritch, Y., and Cohen-Or, D · 2022
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Hierarchical text-conditional image generation with clip latents
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M · 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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Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation
Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M., and Aberman, K · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E., Ghasemipour, S. K. S., Ayan, B. K., Mahdavi, S. S., Lopes, R. G., et al · 2022
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Black-box tuning for language-model-as-a-service
Sun, T., Shao, Y., Qian, H., Huang, X., and Qiu, X · 2022
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Diffusers: State-of-the-art diffusion models
von Platen, P., Patil, S., Lozhkov, A., Cuenca, P., Lambert, N., Rasul, K., Davaadorj, M., and Wolf, T · 2022
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Unifying diffusion models’ latent space, with applications to cyclediffusion and guidance
Wu, C. H. and De la Torre, F · 2022
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Gps: Genetic prompt search for efficient few-shot learning
Xu, H., Chen, Y., Du, Y., Shao, N., Wang, Y., Li, H., and Yang, Z · 2022
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Muse: Text-to-image generation via masked generative transformers
Chang, H., Zhang, H., Barber, J., Maschinot, A., Lezama, J., Jiang, L., Yang, M.-H., Murphy, K., Freeman, W. T., Rubinstein, M., et al · 2023
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