If nothing happens, download GitHub Desktop and try again. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. The abundance of data on the internet is vast. Add a CLIP (Contrastive Language-Image Pre-training) builds on a large body of work on zero-shot transfer, natural language supervision, and multimodal learning.The idea of zero-data learning dates back over a decade [^reference-8] but until recently was mostly studied in computer vision as a way of generalizing to unseen object categories. 27.8 to 16.1. Noisy Student Training extends the idea of self-training and distillation with the use of equal-or-larger student models and noise added to the student during learning. First, we run an EfficientNet-B0 trained on ImageNet[69]. mCE (mean corruption error) is the weighted average of error rate on different corruptions, with AlexNets error rate as a baseline. It is experimentally validated that, for a target test resolution, using a lower train resolution offers better classification at test time, and a simple yet effective and efficient strategy to optimize the classifier performance when the train and test resolutions differ is proposed. and surprising gains on robustness and adversarial benchmarks. The main difference between our work and prior works is that we identify the importance of noise, and aggressively inject noise to make the student better. A tag already exists with the provided branch name. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to 83.7%, reduces ImageNet-C mean corruption error from 45.7 to 28.3, and reduces ImageNet-P mean flip rate from 27.8 to 12.2.Noisy Student Training extends the idea of self-training and distillation with the use of equal-or-larger student models and noise added to the student during learning. Copyright and all rights therein are retained by authors or by other copyright holders. An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. Noisy Student Training seeks to improve on self-training and distillation in two ways. First, a teacher model is trained in a supervised fashion. In all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. We determine number of training steps and the learning rate schedule by the batch size for labeled images. A. Alemi, Thirty-First AAAI Conference on Artificial Intelligence, C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, Rethinking the inception architecture for computer vision, C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, EfficientNet: rethinking model scaling for convolutional neural networks, Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results, H. Touvron, A. Vedaldi, M. Douze, and H. Jgou, Fixing the train-test resolution discrepancy, V. Verma, A. Lamb, J. Kannala, Y. Bengio, and D. Lopez-Paz, Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19), J. Weston, F. Ratle, H. Mobahi, and R. Collobert, Deep learning via semi-supervised embedding, Q. Xie, Z. Dai, E. Hovy, M. Luong, and Q. V. Le, Unsupervised data augmentation for consistency training, S. Xie, R. Girshick, P. Dollr, Z. Tu, and K. He, Aggregated residual transformations for deep neural networks, I.
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