self training with noisy student improves imagenet classification

This paper standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications, and proposes a new dataset called ImageNet-P which enables researchers to benchmark a classifier's robustness to common perturbations. Chum, Label propagation for deep semi-supervised learning, D. P. Kingma, S. Mohamed, D. J. Rezende, and M. Welling, Semi-supervised learning with deep generative models, Semi-supervised classification with graph convolutional networks. 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. 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. Hence, EfficientNet-L0 has around the same training speed with EfficientNet-B7 but more parameters that give it a larger capacity. all 12, Image Classification ImageNet-A test set[25] consists of difficult images that cause significant drops in accuracy to state-of-the-art models. The score is normalized by AlexNets error rate so that corruptions with different difficulties lead to scores of a similar scale. We sample 1.3M images in confidence intervals. 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. 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. unlabeled images. Do imagenet classifiers generalize to imagenet? First, we run an EfficientNet-B0 trained on ImageNet[69]. Here we use unlabeled images to improve the state-of-the-art ImageNet accuracy and show that the accuracy gain has an outsized impact on robustness. sign in 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. It can be seen that masks are useful in improving classification performance. ; 2006)[book reviews], Semi-supervised deep learning with memory, Proceedings of the European Conference on Computer Vision (ECCV), Xception: deep learning with depthwise separable convolutions, K. Clark, M. Luong, C. D. Manning, and Q. V. Le, Semi-supervised sequence modeling with cross-view training, E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le, AutoAugment: learning augmentation strategies from data, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, E. D. Cubuk, B. Zoph, J. Shlens, and Q. V. Le, RandAugment: practical data augmentation with no separate search, Z. Dai, Z. Yang, F. Yang, W. W. Cohen, and R. R. Salakhutdinov, Good semi-supervised learning that requires a bad gan, T. Furlanello, Z. C. Lipton, M. Tschannen, L. Itti, and A. Anandkumar, A. Galloway, A. Golubeva, T. Tanay, M. Moussa, and G. W. Taylor, R. Geirhos, P. Rubisch, C. Michaelis, M. Bethge, F. A. Wichmann, and W. Brendel, ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness, J. Gilmer, L. Metz, F. Faghri, S. S. Schoenholz, M. Raghu, M. Wattenberg, and I. Goodfellow, I. J. Goodfellow, J. Shlens, and C. Szegedy, Explaining and harnessing adversarial examples, Semi-supervised learning by entropy minimization, Advances in neural information processing systems, K. Gu, B. Yang, J. Ngiam, Q. 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. Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. The main difference between our method and knowledge distillation is that knowledge distillation does not consider unlabeled data and does not aim to improve the student model. Flip probability is the probability that the model changes top-1 prediction for different perturbations. We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. The pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). The inputs to the algorithm are both labeled and unlabeled images. This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . 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. Since we use soft pseudo labels generated from the teacher model, when the student is trained to be exactly the same as the teacher model, the cross entropy loss on unlabeled data would be zero and the training signal would vanish. Do better imagenet models transfer better? Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. This paper proposes to search for an architectural building block on a small dataset and then transfer the block to a larger dataset and introduces a new regularization technique called ScheduledDropPath that significantly improves generalization in the NASNet models. To achieve this result, we first train an EfficientNet model on labeled ImageNet images and use it as a teacher to generate pseudo labels on 300M unlabeled images. Test images on ImageNet-P underwent different scales of perturbations. Train a classifier on labeled data (teacher). A tag already exists with the provided branch name. 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. 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. Although noise may appear to be limited and uninteresting, when it is applied to unlabeled data, it has a compound benefit of enforcing local smoothness in the decision function on both labeled and unlabeled data. We thank the Google Brain team, Zihang Dai, Jeff Dean, Hieu Pham, Colin Raffel, Ilya Sutskever and Mingxing Tan for insightful discussions, Cihang Xie for robustness evaluation, Guokun Lai, Jiquan Ngiam, Jiateng Xie and Adams Wei Yu for feedbacks on the draft, Yanping Huang and Sameer Kumar for improving TPU implementation, Ekin Dogus Cubuk and Barret Zoph for help with RandAugment, Yanan Bao, Zheyun Feng and Daiyi Peng for help with the JFT dataset, Olga Wichrowska and Ola Spyra for help with infrastructure. We evaluate the best model, that achieves 87.4% top-1 accuracy, on three robustness test sets: ImageNet-A, ImageNet-C and ImageNet-P. ImageNet-C and P test sets[24] include images with common corruptions and perturbations such as blurring, fogging, rotation and scaling. Self-Training With Noisy Student Improves ImageNet Classification Abstract: 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. Authors: Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le Description: 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. 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. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Self-Training Noisy Student " " Self-Training . 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. Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. But during the learning of the student, we inject noise such as data To achieve this result, we first train an EfficientNet model on labeled The abundance of data on the internet is vast. 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. Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. 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 Due to duplications, there are only 81M unique images among these 130M images. In this work, we showed that it is possible to use unlabeled images to significantly advance both accuracy and robustness of state-of-the-art ImageNet models. Please refer to [24] for details about mCE and AlexNets error rate. 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]). Astrophysical Observatory. We iterate this process by putting back the student as the teacher. 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. on ImageNet ReaL. 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. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. We present a simple self-training method that achieves 87.4 IEEE Transactions on Pattern Analysis and Machine Intelligence. Our procedure went as follows. 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. Finally, in the above, we say that the pseudo labels can be soft or hard. Ranked #14 on Finally, frameworks in semi-supervised learning also include graph-based methods [84, 73, 77, 33], methods that make use of latent variables as target variables [32, 42, 78] and methods based on low-density separation[21, 58, 15], which might provide complementary benefits to our method. Whether the model benefits from more unlabeled data depends on the capacity of the model since a small model can easily saturate, while a larger model can benefit from more data. w Summary of key results compared to previous state-of-the-art models. For instance, on ImageNet-A, Noisy Student achieves 74.2% top-1 accuracy which is approximately 57% more accurate than the previous state-of-the-art model. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. The top-1 accuracy is simply the average top-1 accuracy for all corruptions and all severity degrees. We also list EfficientNet-B7 as a reference. This way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled images. This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. Image Classification The top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. Especially unlabeled images are plentiful and can be collected with ease. 3429-3440. . Noisy Students performance improves with more unlabeled data. The architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. 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. 3.5B weakly labeled Instagram images. The comparison is shown in Table 9. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). Figure 1(a) shows example images from ImageNet-A and the predictions of our models. 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. . . Self-training first uses labeled data to train a good teacher model, then use the teacher model to label unlabeled data and finally use the labeled data and unlabeled data to jointly train a student model. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. During this process, we kept increasing the size of the student model to improve the performance. to noise the student. In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. It implements SemiSupervised Learning with Noise to create an Image Classification. We use the labeled images to train a teacher model using the standard cross entropy loss. Hence, a question that naturally arises is why the student can outperform the teacher with soft 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. 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. Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. task. We then train a larger EfficientNet as a student model on the This result is also a new state-of-the-art and 1% better than the previous best method that used an order of magnitude more weakly labeled data [ 44, 71]. Their main goal is to find a small and fast model for deployment. Code is available at this https URL.Authors: Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. LeLinks:YouTube: https://www.youtube.com/c/yannickilcherTwitter: https://twitter.com/ykilcherDiscord: https://discord.gg/4H8xxDFBitChute: https://www.bitchute.com/channel/yannic-kilcherMinds: https://www.minds.com/ykilcherParler: https://parler.com/profile/YannicKilcherLinkedIn: https://www.linkedin.com/in/yannic-kilcher-488534136/If you want to support me, the best thing to do is to share out the content :)If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this):SubscribeStar (preferred to Patreon): https://www.subscribestar.com/yannickilcherPatreon: https://www.patreon.com/yannickilcherBitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cqEthereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9mMonero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n But training robust supervised learning models is requires this step. In terms of methodology, Computer Science - Computer Vision and Pattern Recognition. In our implementation, labeled images and unlabeled images are concatenated together and we compute the average cross entropy loss. https://arxiv.org/abs/1911.04252. on ImageNet ReaL Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. https://arxiv.org/abs/1911.04252, Accompanying notebook and sources to "A Guide to Pseudolabelling: How to get a Kaggle medal with only one model" (Dec. 2020 PyData Boston-Cambridge Keynote), Deep learning has shown remarkable successes in image recognition in recent years[35, 66, 62, 23, 69]. 10687-10698 Abstract Use Git or checkout with SVN using the web URL. 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 . Their purpose is different from ours: to adapt a teacher model on one domain to another. 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. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. ImageNet . The hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. 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. Work fast with our official CLI. You signed in with another tab or window. These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. Iterative training is not used here for simplicity. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. In other words, small changes in the input image can cause large changes to the predictions. We verify that this is not the case when we use 130M unlabeled images since the model does not overfit the unlabeled set from the training loss. 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. Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. Self-training with Noisy Student. Amongst other components, Noisy Student implements Self-Training in the context of Semi-Supervised Learning. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. We use stochastic depth[29], dropout[63] and RandAugment[14]. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet 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. et al. We do not tune these hyperparameters extensively since our method is highly robust to them. 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. We find that Noisy Student is better with an additional trick: data balancing. 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. Learn more. We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. 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. Code is available at https://github.com/google-research/noisystudent. 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. Our main results are shown in Table1. Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline. Snooze Pineapple Pancake Recipe, Failed Counterintelligence Polygraph, Stevie Smith Motorcycle Accident, Articles S