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We introduce meta-learning algorithms that perform zero-shot weight-space adaptation of neural network models to unseen tasks.
HyperGAN: A Generative Model for Diverse, Performant Neural Networks
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Continual learning with hypernetworks
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Meta-Learning with Warped Gradient Descent, February 2020
Flennerhag, S., Rusu, A. A., Pascanu, R., Visin, F., Yin, H., and Hadsell, R · 1909
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Deep Unsupervised Learning using Nonequilibrium Thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 1938
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Neural networks with late-phase weights
von Oswald, J., Kobayashi, S., Meulemans, A., Henning, C., Grewe, B. F., and Sacramento, J · 2007
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Generative adversarial networks
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Auto-Encoding Variational Bayes
Kingma, D. P. and Welling, M · 2014
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He, K., Zhang, X., Ren, S., and Sun, J · 2015
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U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., and Brox, T · 2015
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Ha, D., Dai, A., and Le, Q. V · 2016
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Optimization as a Model for Few-Shot Learning
Ravi, S. and Larochelle, H · 2016
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Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Finn, C., Abbeel, P., and Levine, S · 2017
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Making the v in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering
Goyal, Y., Khot, T., Summers-Stay, D., Batra, D., and Parikh, D · 2017
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Adam: A Method for Stochastic Optimization, January 2017
Kingma, D. P. and Ba, J · 2017
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FiLM: Visual Reasoning with a General Conditioning Layer, December 2017
Perez, E., Strub, F., de Vries, H., Dumoulin, V., and Courville, A · 2017
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Meta-learning with differentiable closed-form solvers
Bertinetto, L., Henriques, J. F., Torr, P., and Vedaldi, A · 2018
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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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Approximating the Predictive Distribution via Adversarially-Trained Hypernetworks
Henning, C., von Oswald, J., Sacramento, J., Surace, S. C., Pfister, J.-P., and Grewe, B. F · 2018
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Squeeze-and-Excitation Networks
Hu, J., Shen, L., and Sun, G · 2018
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Bayesian Hypernetworks, April 2018
Krueger, D., Huang, C.-W., Islam, R., Turner, R., Lacoste, A., and Courville, A · 2018
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Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace, June 2018
Lee, Y. and Choi, S · 2018
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On First-Order Meta-Learning Algorithms, October 2018
Nichol, A., Achiam, J., and Schulman, J · 2018
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Meta-Learning with Latent Embedding Optimization
Rusu, A. A., Rao, D., Sygnowski, J., Vinyals, O., Pascanu, R., Osindero, S., and Hadsell, R · 2018
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CLIP-Adapter: Better Vision-Language Models with Feature Adapters
Gao, P., Geng, S., Zhang, R., Ma, T., Fang, R., Zhang, Y., Li, H., and Qiao, Y · 2021
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Generative Multi-Label Zero-Shot Learning, January 2021
Gupta, A., Narayan, S., Khan, S., Khan, F. S., Shao, L., and van de Weijer, J · 2021
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Contrastive Embedding for Generalized Zero-Shot Learning
Han, Z., Fu, Z., Chen, S., and Yang, J · 2021
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Posterior Meta-Replay for Continual Learning
Henning, C., Cervera, M. R., D’Angelo, F., von Oswald, J., Traber, R., Ehret, B., Kobayashi, S., Sacramento, J., and Grewe, B. F · 2021
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Classifier-Free Diffusion Guidance
Ho, J. and Salimans, T · 2021
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Adversarial Distillation of Bayesian Neural Network Posteriors
Wang, K.-C., Vicol, P., Lucas, J., Gu, L., Grosse, R., and Zemel, R · 2018
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Multiplicative Interactions and Where to Find Them
Jayakumar, S. M., Czarnecki, W. M., Menick, J., Schwarz, J., Rae, J., Osindero, S., Teh, Y. W., Harley, T., and Pascanu, R · 2019
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Generative Modeling by Estimating Gradients of the Data Distribution
Song, Y. and Ermon, S · 2019
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Fast Context Adaptation via Meta-Learning, June 2019
Zintgraf, L. M., Shiarlis, K., Kurin, V., Hofmann, K., and Whiteson, S · 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., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D · 2020
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Denoising Diffusion Probabilistic Models
Ho, J., Jain, A., and Abbeel, P · 2020
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Neural Tangent Kernel: Convergence and Generalization in Neural Networks, February 2020
Jacot, A., Gabriel, F., and Hongler, C · 2020
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StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery
Patashnik, O., Wu, Z., Shechtman, E., Cohen-Or, D., and Lischinski, D · 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., Krueger, G., and Sutskever, I · 2021
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Audioclip: Extending Clip to Image, Text and Audio
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Planning with Diffusion for Flexible Behavior Synthesis
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Guided-TTS: A Diffusion Model for Text-to-Speech via Classifier Guidance
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On the Effectiveness of Fine-tuning Versus Meta-reinforcement Learning
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GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
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Hierarchical Text-Conditional Image Generation with CLIP Latents
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High-Resolution Image Synthesis With Latent Diffusion Models
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Hyper-Representations as Generative Models: Sampling Unseen Neural Network Weights, September 2022
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Distinguishing Unseen from Seen for Generalized Zero-shot Learning
Su, H., Li, J., Chen, Z., Zhu, L., and Lu, K · 2022
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HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning, July 2022
Zhmoginov, A., Sandler, M., and Vladymyrov, M · 2022
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