self training with noisy student improves imagenet classification self training with noisy student improves imagenet classification

Hence we use soft pseudo labels for our experiments unless otherwise specified. - : self-training_with_noisy_student_improves_imagenet_classification At the top-left image, the model without Noisy Student ignores the sea lions and mistakenly recognizes a buoy as a lighthouse, while the model with Noisy Student can recognize the sea lions. [68, 24, 55, 22]. The most interesting image is shown on the right of the first row. Self-training with Noisy Student improves ImageNet classification This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. We will then show our results on ImageNet and compare them with state-of-the-art models. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. Although they have produced promising results, in our preliminary experiments, consistency regularization works less well on ImageNet because consistency regularization in the early phase of ImageNet training regularizes the model towards high entropy predictions, and prevents it from achieving good accuracy. Summarization_self-training_with_noisy_student_improves_imagenet_classification. 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. Our study shows that using unlabeled data improves accuracy and general robustness. A tag already exists with the provided branch name. However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. In all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. In typical self-training with the teacher-student framework, noise injection to the student is not used by default, or the role of noise is not fully understood or justified. We then train a larger EfficientNet as a student model on the [2] show that Self-Training is superior to Pre-training with ImageNet Supervised Learning on a few Computer . Especially unlabeled images are plentiful and can be collected with ease. Self-Training With Noisy Student Improves ImageNet Classification Noisy Student Explained | Papers With Code 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. Note that these adversarial robustness results are not directly comparable to prior works since we use a large input resolution of 800x800 and adversarial vulnerability can scale with the input dimension[17, 20, 19, 61]. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. Code for Noisy Student Training. Ranked #14 on sign in We iterate this process by putting back the student as the teacher. Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. Soft pseudo labels lead to better performance for low confidence data. 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. However, manually annotating organs from CT scans is time . Lastly, we apply the recently proposed technique to fix train-test resolution discrepancy[71] for EfficientNet-L0, L1 and L2. labels, the teacher is not noised so that the pseudo labels are as good as to use Codespaces. Self-Training With Noisy Student Improves ImageNet Classification Self-training with Noisy Student improves ImageNet classification Abstract. 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. 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. CLIP: Connecting text and images - OpenAI 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. This way, the pseudo labels are as good as possible, and the noised student is forced to learn harder from the pseudo labels. 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. 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. Zoph et al. Figure 1(b) shows images from ImageNet-C and the corresponding predictions. We apply dropout to the final classification layer with a dropout rate of 0.5. sign in (using extra training data). We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. . Noisy Student Training is a semi-supervised learning approach. over the JFT dataset to predict a label for each image. 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. https://arxiv.org/abs/1911.04252. self-mentoring outperforms data augmentation and self training. Abdominal organ segmentation is very important for clinical applications. This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . 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. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Train a classifier on labeled data (teacher). For RandAugment, we apply two random operations with the magnitude set to 27. Infer labels on a much larger unlabeled dataset. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Different kinds of noise, however, may have different effects. Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer . Different types of. A common workaround is to use entropy minimization or ramp up the consistency loss. 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. 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). 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. 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. Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. 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. We obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. We investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. 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. For unlabeled images, we set the batch size to be three times the batch size of labeled images for large models, including EfficientNet-B7, L0, L1 and L2. Self-Training With Noisy Student Improves ImageNet Classification. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. To achieve this result, we first train an EfficientNet model on labeled Train a classifier on labeled data (teacher). Train a larger classifier on the combined set, adding noise (noisy student). Using self-training with Noisy Student, together with 300M unlabeled images, we improve EfficientNets[69] ImageNet top-1 accuracy to 87.4%. By clicking accept or continuing to use the site, you agree to the terms outlined in our. 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. Noisy Student self-training is an effective way to leverage unlabelled datasets and improving accuracy by adding noise to the student model while training so it learns beyond the teacher's knowledge. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images augmentation, dropout, stochastic depth to the student so that the noised However, 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. In other words, the student is forced to mimic a more powerful ensemble model. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. We first report the validation set accuracy on the ImageNet 2012 ILSVRC challenge prediction task as commonly done in literature[35, 66, 23, 69] (see also [55]). On robustness test sets, it improves ImageNet-A top . We sample 1.3M images in confidence intervals. Self-training Yalniz et al. Noise Self-training with Noisy Student 1. This invariance constraint reduces the degrees of freedom in the model. 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. Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. This paper reviews the state-of-the-art in both the field of CNNs for image classification and object detection and Autonomous Driving Systems (ADSs) in a synergetic way including a comprehensive trade-off analysis from a human-machine perspective. In our implementation, labeled images and unlabeled images are concatenated together and we compute the average cross entropy loss. The pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). We improved it by adding noise to the student to learn beyond the teachers knowledge. 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. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. Then, that teacher is used to label the unlabeled data. Code is available at https://github.com/google-research/noisystudent. First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. Probably due to the same reason, at =16, EfficientNet-L2 achieves an accuracy of 1.1% under a stronger attack PGD with 10 iterations[43], which is far from the SOTA results. Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. This material is presented to ensure timely dissemination of scholarly and technical work. 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. A novel random matrix theory based damping learner for second order optimisers inspired by linear shrinkage estimation is developed, and it is demonstrated that the derived method works well with adaptive gradient methods such as Adam. 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. You signed in with another tab or window. Self-training with Noisy Student improves ImageNet classification About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . We use EfficientNet-B4 as both the teacher and the student. Especially unlabeled images are plentiful and can be collected with ease. We use the standard augmentation instead of RandAugment in this experiment. On ImageNet-P, it leads to an mean flip rate (mFR) of 17.8 if we use a resolution of 224x224 (direct comparison) and 16.1 if we use a resolution of 299x299.111For EfficientNet-L2, we use the model without finetuning with a larger test time resolution, since a larger resolution results in a discrepancy with the resolution of data and leads to degraded performance on ImageNet-C and ImageNet-P. We iterate this process by putting back the student as the teacher. On, International journal of molecular sciences. Use Git or checkout with SVN using the web URL. This is a recurring payment that will happen monthly, If you exceed more than 500 images, they will be charged at a rate of $5 per 500 images. Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10687-10698, (2020 . The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). 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. Do better imagenet models transfer better? Since a teacher models confidence on an image can be a good indicator of whether it is an out-of-domain image, we consider the high-confidence images as in-domain images and the low-confidence images as out-of-domain images.