Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet and surprising gains on robustness and adversarial benchmarks. (Submitted on 11 Nov 2019) We present a simple self-training method that achieves 87.4% top-1 accuracy on ImageNet, which is 1.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. Self-Training With Noisy Student Improves ImageNet Classification On ImageNet, we first train an EfficientNet model on labeled images and use it as a teacher to generate pseudo labels for 300M unlabeled images. Noisy StudentImageNetEfficientNet-L2state-of-the-art. We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. tsai - Noisy student Compared to consistency training[45, 5, 74], the self-training / teacher-student framework is better suited for ImageNet because we can train a good teacher on ImageNet using label data. Self-training with Noisy Student improves ImageNet classication Qizhe Xie 1, Minh-Thang Luong , Eduard Hovy2, Quoc V. Le1 1Google Research, Brain Team, 2Carnegie Mellon University fqizhex, thangluong, qvlg@google.com, hovy@cmu.edu Abstract We present Noisy Student Training, a semi-supervised learning approach that works well even when . SelfSelf-training with Noisy Student improves ImageNet classification all 12, Image Classification Our experiments showed that self-training with Noisy Student and EfficientNet can achieve an accuracy of 87.4% which is 1.9% higher than without Noisy Student. FixMatch-LS: Semi-supervised skin lesion classification with label Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled data. Papers With Code is a free resource with all data licensed under. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: For ImageNet checkpoints trained by Noisy Student Training, please refer to the EfficientNet github. Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a Chowdhury et al. Self-Training for Natural Language Understanding! 27.8 to 16.1. Ranked #14 on The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. Finally, we iterate the algorithm a few times by treating the student as a teacher to generate new pseudo labels and train a new student. Their noise model is video specific and not relevant for image classification. Self-training is a form of semi-supervised learning [10] which attempts to leverage unlabeled data to improve classification performance in the limited data regime. Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. Self-training with Noisy Student improves ImageNet classification unlabeled images. If nothing happens, download GitHub Desktop and try again. Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . Our study shows that using unlabeled data improves accuracy and general robustness. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. (2) With out-of-domain unlabeled images, hard pseudo labels can hurt the performance while soft pseudo labels leads to robust performance. Proceedings of the eleventh annual conference on Computational learning theory, Proceedings of the IEEE conference on computer vision and pattern recognition, Empirical Methods in Natural Language Processing (EMNLP), Imagenet classification with deep convolutional neural networks, Domain adaptive transfer learning with specialist models, Thirty-Second AAAI Conference on Artificial Intelligence, Regularized evolution for image classifier architecture search, Inception-v4, inception-resnet and the impact of residual connections on learning. We conduct experiments on ImageNet 2012 ILSVRC challenge prediction task since it has been considered one of the most heavily benchmarked datasets in computer vision and that improvements on ImageNet transfer to other datasets. Self-Training with Noisy Student Improves ImageNet Classification In Noisy Student, we combine these two steps into one because it simplifies the algorithm and leads to better performance in our preliminary experiments. To intuitively understand the significant improvements on the three robustness benchmarks, we show several images in Figure2 where the predictions of the standard model are incorrect and the predictions of the Noisy Student model are correct. On . Self-training with noisy student improves imagenet classification. Notably, EfficientNet-B7 achieves an accuracy of 86.8%, which is 1.8% better than the supervised model. The performance drops when we further reduce it. We find that Noisy Student is better with an additional trick: data balancing. As shown in Table3,4 and5, when compared with the previous state-of-the-art model ResNeXt-101 WSL[44, 48] trained on 3.5B weakly labeled images, Noisy Student yields substantial gains on robustness datasets. However an important requirement for Noisy Student to work well is that the student model needs to be sufficiently large to fit more data (labeled and pseudo labeled). Their framework is highly optimized for videos, e.g., prediction on which frame to use in a video, which is not as general as our work. EfficientNet with Noisy Student produces correct top-1 predictions (shown in. 2023.3.1_2 - This work introduces two challenging datasets that reliably cause machine learning model performance to substantially degrade and curates an adversarial out-of-distribution detection dataset called IMAGENET-O, which is the first out- of-dist distribution detection dataset created for ImageNet models. Med. However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. Are labels required for improving adversarial robustness? In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. In our experiments, we observe that soft pseudo labels are usually more stable and lead to faster convergence, especially when the teacher model has low accuracy. We used the version from [47], which filtered the validation set of ImageNet. Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. In the following, we will first describe experiment details to achieve our results. Self-training 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. E. Arazo, D. Ortego, P. Albert, N. E. OConnor, and K. McGuinness, Pseudo-labeling and confirmation bias in deep semi-supervised learning, B. Athiwaratkun, M. Finzi, P. Izmailov, and A. G. Wilson, There are many consistent explanations of unlabeled data: why you should average, International Conference on Learning Representations, Advances in Neural Information Processing Systems, D. Berthelot, N. Carlini, I. Goodfellow, N. Papernot, A. Oliver, and C. Raffel, MixMatch: a holistic approach to semi-supervised learning, Combining labeled and unlabeled data with co-training, C. Bucilu, R. Caruana, and A. Niculescu-Mizil, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, Y. Carmon, A. Raghunathan, L. Schmidt, P. Liang, and J. C. Duchi, Unlabeled data improves adversarial robustness, Semi-supervised learning (chapelle, o. et al., eds. We investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. The architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. CLIP: Connecting text and images - OpenAI Diagnostics | Free Full-Text | A Collaborative Learning Model for Skin If nothing happens, download Xcode and try again. [68, 24, 55, 22]. Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. While removing noise leads to a much lower training loss for labeled images, we observe that, for unlabeled images, removing noise leads to a smaller drop in training loss. Our experiments showed that self-training with Noisy Student and EfficientNet can achieve an accuracy of 87.4% which is 1.9% higher than without Noisy Student. Self-Training With Noisy Student Improves ImageNet Classification Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. A tag already exists with the provided branch name. ImageNet-A top-1 accuracy from 16.6 We use the standard augmentation instead of RandAugment in this experiment. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). . This shows that it is helpful to train a large model with high accuracy using Noisy Student when small models are needed for deployment. We train our model using the self-training framework[59] which has three main steps: 1) train a teacher model on labeled images, 2) use the teacher to generate pseudo labels on unlabeled images, and 3) train a student model on the combination of labeled images and pseudo labeled images. A semi-supervised segmentation network based on noisy student learning 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. Self-training with Noisy Student improves ImageNet classification A. Krizhevsky, I. Sutskever, and G. E. Hinton, Temporal ensembling for semi-supervised learning, Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks, Workshop on Challenges in Representation Learning, ICML, Certainty-driven consistency loss for semi-supervised learning, C. Liu, B. Zoph, M. Neumann, J. Shlens, W. Hua, L. Li, L. Fei-Fei, A. Yuille, J. Huang, and K. Murphy, R. G. Lopes, D. Yin, B. Poole, J. Gilmer, and E. D. Cubuk, Improving robustness without sacrificing accuracy with patch gaussian augmentation, Y. Luo, J. Zhu, M. Li, Y. Ren, and B. Zhang, Smooth neighbors on teacher graphs for semi-supervised learning, L. Maale, C. K. Snderby, S. K. Snderby, and O. Winther, A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, Towards deep learning models resistant to adversarial attacks, D. Mahajan, R. Girshick, V. Ramanathan, K. He, M. Paluri, Y. Li, A. Bharambe, and L. van der Maaten, Exploring the limits of weakly supervised pretraining, T. Miyato, S. Maeda, S. Ishii, and M. Koyama, Virtual adversarial training: a regularization method for supervised and semi-supervised learning, IEEE transactions on pattern analysis and machine intelligence, A. Najafi, S. Maeda, M. Koyama, and T. Miyato, Robustness to adversarial perturbations in learning from incomplete data, J. Ngiam, D. Peng, V. Vasudevan, S. Kornblith, Q. V. Le, and R. Pang, Robustness properties of facebooks resnext wsl models, Adversarial dropout for supervised and semi-supervised learning, Lessons from building acoustic models with a million hours of speech, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), S. Qiao, W. Shen, Z. Zhang, B. Wang, and A. Yuille, Deep co-training for semi-supervised image recognition, I. Radosavovic, P. Dollr, R. Girshick, G. Gkioxari, and K. He, Data distillation: towards omni-supervised learning, A. Rasmus, M. Berglund, M. Honkala, H. Valpola, and T. Raiko, Semi-supervised learning with ladder networks, E. Real, A. Aggarwal, Y. Huang, and Q. V. Le, Proceedings of the AAAI Conference on Artificial Intelligence, B. Recht, R. Roelofs, L. Schmidt, and V. Shankar. Self-training with Noisy Student improves ImageNet classification Especially unlabeled images are plentiful and can be collected with ease. arXiv:1911.04252v4 [cs.LG] 19 Jun 2020 to use Codespaces. Callback to apply noisy student self-training (a semi-supervised learning approach) based on: Xie, Q., Luong, M. T., Hovy, E., & Le, Q. V. (2020). Scripts used for our ImageNet experiments: Similar scripts to run predictions on unlabeled data, filter and balance data and train using the filtered data. The main difference between Data Distillation and our method is that we use the noise to weaken the student, which is the opposite of their approach of strengthening the teacher by ensembling. The architectures for the student and teacher models can be the same or different. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. . Noisy Students performance improves with more unlabeled data. Le. on ImageNet, which is 1.0 Are you sure you want to create this branch? A common workaround is to use entropy minimization or ramp up the consistency loss. The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. The performance consistently drops with noise function removed. During the generation of the pseudo This work proposes a novel architectural unit, which is term the Squeeze-and-Excitation (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels and shows that these blocks can be stacked together to form SENet architectures that generalise extremely effectively across different datasets.