self training with noisy student improves imagenet classification

Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. Noisy Student Training achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. Code for Noisy Student Training. Our experiments show that an important element for this simple method to work well at scale is that the student model should be noised during its training while the teacher should not be noised during the generation of pseudo labels. This shows that it is helpful to train a large model with high accuracy using Noisy Student when small models are needed for deployment. [^reference-9] [^reference-10] A critical insight was to . Use a model to predict pseudo-labels on the filtered data: This is not an officially supported Google product. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. Significantly, after using the masks generated by student-SN, the classification performance improved by 0.9 of AC, 0.7 of SE, and 0.9 of AUC. First, we run an EfficientNet-B0 trained on ImageNet[69]. to use Codespaces. On ImageNet-C, it reduces mean corruption error (mCE) from 45.7 to 31.2. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. A self-training method that better adapt to the popular two stage training pattern for multi-label text classification under a semi-supervised scenario by continuously finetuning the semantic space toward increasing high-confidence predictions, intending to further promote the performance on target tasks. We obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), We present a simple self-training method that achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. Similar to[71], we fix the shallow layers during finetuning. Our study shows that using unlabeled data improves accuracy and general robustness. Noisy Student Training achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. These test sets are considered as robustness benchmarks because the test images are either much harder, for ImageNet-A, or the test images are different from the training images, for ImageNet-C and P. For ImageNet-C and ImageNet-P, we evaluate our models on two released versions with resolution 224x224 and 299x299 and resize images to the resolution EfficientNet is trained on. Train a larger classifier on the combined set, adding noise (noisy student). . Edit social preview. The accuracy is improved by about 10% in most settings. This work systematically benchmark state-of-the-art methods that use unlabeled data, including domain-invariant, self-training, and self-supervised methods, and shows that their success on WILDS is limited. For RandAugment, we apply two random operations with the magnitude set to 27. We duplicate images in classes where there are not enough images. Sun, Z. Liu, D. Sedra, and K. Q. Weinberger, Y. Huang, Y. Cheng, D. Chen, H. Lee, J. Ngiam, Q. V. Le, and Z. Chen, GPipe: efficient training of giant neural networks using pipeline parallelism, A. Iscen, G. Tolias, Y. Avrithis, and O. all 12, Image Classification 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. 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. Notably, EfficientNet-B7 achieves an accuracy of 86.8%, which is 1.8% better than the supervised model. See EfficientNet-L0 is wider and deeper than EfficientNet-B7 but uses a lower resolution, which gives it more parameters to fit a large number of unlabeled images with similar training speed. Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. The baseline model achieves an accuracy of 83.2. We vary the model size from EfficientNet-B0 to EfficientNet-B7[69] and use the same model as both the teacher and the student. Are you sure you want to create this branch? 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . For instance, on ImageNet-1k, Layer Grafted Pre-training yields 65.5% Top-1 accuracy in terms of 1% few-shot learning with ViT-B/16, which improves MIM and CL baselines by 14.4% and 2.1% with no bells and whistles. Figure 1(a) shows example images from ImageNet-A and the predictions of our models. 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. Using Noisy Student (EfficientNet-L2) as the teacher leads to another 0.8% improvement on top of the improved results. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. Noisy Student Training achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. Train a classifier on labeled data (teacher). The score is normalized by AlexNets error rate so that corruptions with different difficulties lead to scores of a similar scale. For instance, on the right column, as the image of the car undergone a small rotation, the standard model changes its prediction from racing car to car wheel to fire engine. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (teacher). Le, and J. Shlens, Using videos to evaluate image model robustness, Deep residual learning for image recognition, Benchmarking neural network robustness to common corruptions and perturbations, D. Hendrycks, K. Zhao, S. Basart, J. Steinhardt, and D. Song, Distilling the knowledge in a neural network, G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, G. Huang, Y. But during the learning of the student, we inject noise such as data Papers With Code is a free resource with all data licensed under. As can be seen from the figure, our model with Noisy Student makes correct predictions for images under severe corruptions and perturbations such as snow, motion blur and fog, while the model without Noisy Student suffers greatly under these conditions. The mapping from the 200 classes to the original ImageNet classes are available online.222https://github.com/hendrycks/natural-adv-examples/blob/master/eval.py. We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. Different kinds of noise, however, may have different effects. [50] used knowledge distillation on unlabeled data to teach a small student model for speech recognition. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). - : self-training_with_noisy_student_improves_imagenet_classification We use a resolution of 800x800 in this experiment. The hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. Hence we use soft pseudo labels for our experiments unless otherwise specified. , have shown that computer vision models lack robustness. This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . In this section, we study the importance of noise and the effect of several noise methods used in our model. Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled 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. For classes where we have too many images, we take the images with the highest confidence. Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. Chowdhury et al. A tag already exists with the provided branch name. Noisy Student leads to significant improvements across all model sizes for EfficientNet. If nothing happens, download GitHub Desktop and try again. When the student model is deliberately noised it is actually trained to be consistent to the more powerful teacher model that is not noised when it generates pseudo labels. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. possible. It is expensive and must be done with great care. We find that Noisy Student is better with an additional trick: data balancing. 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. 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. Z. Yalniz, H. Jegou, K. Chen, M. Paluri, and D. Mahajan, Billion-scale semi-supervised learning for image classification, Z. Yang, W. W. Cohen, and R. Salakhutdinov, Revisiting semi-supervised learning with graph embeddings, Z. Yang, J. Hu, R. Salakhutdinov, and W. W. Cohen, Semi-supervised qa with generative domain-adaptive nets, Unsupervised word sense disambiguation rivaling supervised methods, 33rd annual meeting of the association for computational linguistics, R. Zhai, T. Cai, D. He, C. Dan, K. He, J. Hopcroft, and L. Wang, Adversarially robust generalization just requires more unlabeled data, X. Zhai, A. Oliver, A. Kolesnikov, and L. Beyer, Proceedings of the IEEE international conference on computer vision, Making convolutional networks shift-invariant again, X. Zhang, Z. Li, C. Change Loy, and D. Lin, Polynet: a pursuit of structural diversity in very deep networks, X. Zhu, Z. Ghahramani, and J. D. Lafferty, Semi-supervised learning using gaussian fields and harmonic functions, Proceedings of the 20th International conference on Machine learning (ICML-03), Semi-supervised learning literature survey, University of Wisconsin-Madison Department of Computer Sciences, B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le, Learning transferable architectures for scalable image recognition, Architecture specifications for EfficientNet used in the paper. The pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. Self-training with Noisy Student. Work fast with our official CLI. Learn more. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet Hence, EfficientNet-L0 has around the same training speed with EfficientNet-B7 but more parameters that give it a larger capacity. We apply dropout to the final classification layer with a dropout rate of 0.5. In addition to improving state-of-the-art results, we conduct additional experiments to verify if Noisy Student can benefit other EfficienetNet models. The abundance of data on the internet is vast. 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. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. We also study the effects of using different amounts of unlabeled data. Code for Noisy Student Training. Noisy Students performance improves with more unlabeled data. Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. Noisy Student (B7) means to use EfficientNet-B7 for both the student and the teacher. Flip probability is the probability that the model changes top-1 prediction for different perturbations. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. IEEE Trans. Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. on ImageNet ReaL During this process, we kept increasing the size of the student model to improve the performance. Selected images from robustness benchmarks ImageNet-A, C and P. Test images from ImageNet-C underwent artificial transformations (also known as common corruptions) that cannot be found on the ImageNet training set. 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. Our procedure went as follows. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. 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. Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical 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. on ImageNet ReaL. 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. ImageNet images and use it as a teacher to generate pseudo labels on 300M 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. 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.

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