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Nature is infinitely resolution-free.
Estimation of non-normalized statistical models by score matching
Hyvärinen, A. and Dayan, P · 2005
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Denoising diffusion implicit models
Song, J., Meng, C., and Ermon, S · 2010
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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 · 2011
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Improved techniques for training gans
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 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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Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Large scale gan training for high fidelity natural image synthesis
Brock, A., Donahue, J., and Simonyan, K · 2018
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Improved precision and recall metric for assessing generative models
Kynkäänniemi, T., Karras, T., Laine, S., and Lehtinen, J.and Aila, T · 2019
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Fixing the train-test resolution discrepancy
Touvron, H., Vedaldi, A., Douze, M., and Jégou, H · 2019
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Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Glu variants improve transformer
Shazeer, N · 2020
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Diffusion models beat gans on image synthesis
Dhariwal, P. and Nichol, A · 2021
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Taming transformers for high-resolution image synthesis
Esser, P., Rombach, R., and Ommer, B · 2021
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Classifier-free diffusion guidance
Ho, J. and Salimans, T · 2021
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Swin transformer: Hierarchical vision transformer using shifted windows
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B · 2021
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Generating images with sparse representations
Nash, C., Menick, J., Dieleman, S., and Battaglia, P. W · 2021
A length-extrapolatable transformer
Sun, Y., Dong, L., Patra, B., Ma, S., Huang, S., Benhaim, A., Chaudhary, V., Song, X., , and Wei, F · 2022
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All are worth words: A vit backbone for diffusion models
Bao, F., Nie, S., Xue, K., Cao, Y., Li, C., Su, H., and Zhu, J · 2023
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Extending context window of large language models via positional interpolation
Chen, S., Wong, S., Chen, L., and Tian, Y · 2023
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Patch n’pack: Navit, a vision transformer for any aspect ratio and resolution
Dehghani, M., Mustafa, B., Djolonga, J., Heek, J., Minderer, M., Caron, M., Steiner, A., Puigcerver, J., Geirhos, R., Alabdulmohsin, I., et al · 2023
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Cited alongside, same era.
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
Cited alongside, same era.
Training data-efficient image transformers & distillation through attention
Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., and Jégou, H · 2021
Cited alongside, same era.
Maskgit: Masked generative image transformer
Chang, H., Zhang, H., Jiang, L., Liu, C., and Freeman, W. T · 2022
Cited alongside, same era.
Cascaded diffusion models for high fidelity image generation
Ho, J., Saharia, C., Chan, W., Fleet, D. J., Norouzi, M., and Salimans, T · 2022
Cited alongside, same era.
A convnet for the 2020s
Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., and Xie, S · 2022
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
Cited alongside, same era.
Gao, S., Zhou, P., Cheng, M.-M., and Yan, S · 2023
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Diffit: Diffusion vision transformers for image generation
Hatamizadeh, A., Song, J., Liu, G., Kautz, J., and Vahdat, A · 2023
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Scaling laws of rope-based extrapolation
Liu, X., Yan, H., Zhang, S., An, C., Qiu, X., and Lin, D · 2023
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Scalable diffusion models with transformers
Peebles, W. and Xie, S · 2023
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Yarn: Efficient context window extension of large language models
Peng, B., Quesnelle, J., Fan, H., and Shippole, E · 2023
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Randomized positional encodings boost length generalization of transformers
Ruoss, A., Delétang, G., Genewein, T., Grau-Moya, J., Csordás, R., Bennani, M., Legg, S., and Veness, J · 2023
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Gemini: a family of highly capable multimodal models
Team, G., Anil, R., Borgeaud, S., Wu, Y., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., et al · 2023
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Ntk-aware scaled rope allows llama models to have extended (8k+) context size without any fine-tuning and minimal perplexity degradation
LocalLLaMA · 2024
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Roformer: Enhanced transformer with rotary position embedding
Su, J., Ahmed, M., Lu, Y., Pan, S., Bo, W., and Liu, Y · 2024
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