augmentation, dropout, stochastic depth to the student so that the noised We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Train a classifier on labeled data (teacher). Noisy Student Training seeks to improve on self-training and distillation in two ways. Self-training with Noisy Student improves ImageNet classification Abstract. Self-Training With Noisy Student Improves ImageNet Classification 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. Self-training with Noisy Student improves ImageNet classification We obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. In particular, we set the survival probability in stochastic depth to 0.8 for the final layer and follow the linear decay rule for other layers. Yalniz et al. mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline. (using extra training data). Self-training with Noisy Student improves ImageNet classification (2) With out-of-domain unlabeled images, hard pseudo labels can hurt the performance while soft pseudo labels leads to robust performance. We iterate this process by putting back the student as the teacher. Their noise model is video specific and not relevant for image classification. International Conference on Machine Learning, Learning extraction patterns for subjective expressions, Proceedings of the 2003 conference on Empirical methods in natural language processing, A. Roy Chowdhury, P. Chakrabarty, A. Singh, S. Jin, H. Jiang, L. Cao, and E. G. Learned-Miller, Automatic adaptation of object detectors to new domains using self-training, T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, Probability of error of some adaptive pattern-recognition machines, W. Shi, Y. Gong, C. Ding, Z. MaXiaoyu Tao, and N. Zheng, Transductive semi-supervised deep learning using min-max features, C. Simon-Gabriel, Y. Ollivier, L. Bottou, B. Schlkopf, and D. Lopez-Paz, First-order adversarial vulnerability of neural networks and input dimension, Very deep convolutional networks for large-scale image recognition, N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: a simple way to prevent neural networks from overfitting. Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and Noisy Student Training is a semi-supervised learning approach. ImageNet-A test set[25] consists of difficult images that cause significant drops in accuracy to state-of-the-art models. Aerial Images Change Detection, Multi-Task Self-Training for Learning General Representations, Self-Training Vision Language BERTs with a Unified Conditional Model, 1Cademy @ Causal News Corpus 2022: Leveraging Self-Training in Causality This paper presents a unique study of transfer learning with large convolutional networks trained to predict hashtags on billions of social media images and shows improvements on several image classification and object detection tasks, and reports the highest ImageNet-1k single-crop, top-1 accuracy to date. These CVPR 2020 papers are the Open Access versions, provided by the. 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. 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. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. It is found that training and scaling strategies may matter more than architectural changes, and further, that the resulting ResNets match recent state-of-the-art models. Self-training with Noisy Student improves ImageNet classification In contrast, the predictions of the model with Noisy Student remain quite stable. Ranked #14 on Self-Training with Noisy Student Improves ImageNet Classification We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. As shown in Figure 1, Noisy Student leads to a consistent improvement of around 0.8% for all model sizes. If you get a better model, you can use the model to predict pseudo-labels on the filtered data. Here we show the evidence in Table 6, noise such as stochastic depth, dropout and data augmentation plays an important role in enabling the student model to perform better than the teacher. . All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (teacher). Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. Learn more. Self-Training With Noisy Student Improves ImageNet Classification Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. For example, with all noise removed, the accuracy drops from 84.9% to 84.3% in the case with 130M unlabeled images and drops from 83.9% to 83.2% in the case with 1.3M unlabeled images. "Self-training with Noisy Student improves ImageNet classification" pytorch implementation. The abundance of data on the internet is vast. possible. During the learning of the student, we inject noise such as dropout, stochastic depth, and data augmentation via RandAugment to the student so that the student generalizes better than the teacher. This shows that it is helpful to train a large model with high accuracy using Noisy Student when small models are needed for deployment. The proposed use of distillation to only handle easy instances allows for a more aggressive trade-off in the student size, thereby reducing the amortized cost of inference and achieving better accuracy than standard distillation. self-mentoring outperforms data augmentation and self training. As we use soft targets, our work is also related to methods in Knowledge Distillation[7, 3, 26, 16]. EfficientNet with Noisy Student produces correct top-1 predictions (shown in. Addressing the lack of robustness has become an important research direction in machine learning and computer vision in recent years. On ImageNet-C, it reduces mean corruption error (mCE) from 45.7 to 31.2. Amongst other components, Noisy Student implements Self-Training in the context of Semi-Supervised Learning. Notice, Smithsonian Terms of The algorithm is basically self-training, a method in semi-supervised learning (. The best model in our experiments is a result of iterative training of teacher and student by putting back the student as the new teacher to generate new pseudo labels. sign in First, we run an EfficientNet-B0 trained on ImageNet[69]. This paper proposes a pipeline, based on a teacher/student paradigm, that leverages a large collection of unlabelled images to improve the performance for a given target architecture, like ResNet-50 or ResNext. Noisy Student Training is a semi-supervised learning method which achieves 88.4% top-1 accuracy on ImageNet (SOTA) and surprising gains on robustness and adversarial benchmarks. The biggest gain is observed on ImageNet-A: our method achieves 3.5x higher accuracy on ImageNet-A, going from 16.6% of the previous state-of-the-art to 74.2% top-1 accuracy. When dropout and stochastic depth are used, the teacher model behaves like an ensemble of models (when it generates the pseudo labels, dropout is not used), whereas the student behaves like a single model. Self-training with Noisy Student - Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. The paradigm of pre-training on large supervised datasets and fine-tuning the weights on the target task is revisited, and a simple recipe that is called Big Transfer (BiT) is created, which achieves strong performance on over 20 datasets. Astrophysical Observatory. When data augmentation noise is used, the student must ensure that a translated image, for example, should have the same category with a non-translated image. 27.8 to 16.1. Apart from self-training, another important line of work in semi-supervised learning[9, 85] is based on consistency training[6, 4, 53, 36, 70, 45, 41, 51, 10, 12, 49, 2, 38, 72, 74, 5, 81]. Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. We use EfficientNet-B0 as both the teacher model and the student model and compare using Noisy Student with soft pseudo labels and hard pseudo labels. The performance consistently drops with noise function removed. We do not tune these hyperparameters extensively since our method is highly robust to them. The main use case of knowledge distillation is model compression by making the student model smaller. It can be seen that masks are useful in improving classification performance. We iterate this process by putting back the student as the teacher. Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer . We duplicate images in classes where there are not enough images. In particular, we first perform normal training with a smaller resolution for 350 epochs. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. [57] used self-training for domain adaptation. As shown in Table2, Noisy Student with EfficientNet-L2 achieves 87.4% top-1 accuracy which is significantly better than the best previously reported accuracy on EfficientNet of 85.0%. 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.