Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. 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. 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. Summarization_self-training_with_noisy_student_improves_imagenet_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. 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. https://arxiv.org/abs/1911.04252. For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 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. 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. 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. Hence we use soft pseudo labels for our experiments unless otherwise specified. Use Git or checkout with SVN using the web URL. IEEE Transactions on Pattern Analysis and Machine Intelligence. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. As we use soft targets, our work is also related to methods in Knowledge Distillation[7, 3, 26, 16]. Different types of. 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. Ranked #14 on Iterative training is not used here for simplicity. ImageNet-A top-1 accuracy from 16.6 We determine number of training steps and the learning rate schedule by the batch size for labeled images. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. 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). The abundance of data on the internet is vast. To noise the student, we use dropout[63], data augmentation[14] and stochastic depth[29] during its training. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Zoph et al. 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 . As can be seen from Table 8, the performance stays similar when we reduce the data to 116 of the total data, which amounts to 8.1M images after duplicating. A common workaround is to use entropy minimization or ramp up the consistency loss. 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. , have shown that computer vision models lack robustness. Noisy Student can still improve the accuracy to 1.6%. The pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). Infer labels on a much larger unlabeled dataset. 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. Using Noisy Student (EfficientNet-L2) as the teacher leads to another 0.8% improvement on top of the improved results. Self-Training With Noisy Student Improves ImageNet Classification @article{Xie2019SelfTrainingWN, title={Self-Training With Noisy Student Improves ImageNet Classification}, author={Qizhe Xie and Eduard H. Hovy and Minh-Thang Luong and Quoc V. Le}, journal={2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2019 . 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. We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. Noisy Student Training seeks to improve on self-training and distillation in two ways. 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. 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 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 will then show our results on ImageNet and compare them with state-of-the-art models. We used the version from [47], which filtered the validation set of ImageNet. (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. EfficientNet-L1 approximately doubles the training time of EfficientNet-L0. The architectures for the student and teacher models can be the same or different. Noisy Students performance improves with more unlabeled data. [57] used self-training for domain adaptation. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. Not only our method improves standard ImageNet accuracy, it also improves classification robustness on much harder test sets by large margins: ImageNet-A[25] top-1 accuracy from 16.6% to 74.2%, ImageNet-C[24] mean corruption error (mCE) from 45.7 to 31.2 and ImageNet-P[24] mean flip rate (mFR) from 27.8 to 16.1. This is probably because it is harder to overfit the large unlabeled dataset. Especially unlabeled images are plentiful and can be collected with ease. 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. 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. In other words, small changes in the input image can cause large changes to the predictions. We then train a larger EfficientNet as a student model on the This invariance constraint reduces the degrees of freedom in the model. 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]. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model . For a small student model, using our best model Noisy Student (EfficientNet-L2) as the teacher model leads to more improvements than using the same model as the teacher, which shows that it is helpful to push the performance with our method when small models are needed for deployment. Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a The baseline model achieves an accuracy of 83.2. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. In our implementation, labeled images and unlabeled images are concatenated together and we compute the average cross entropy loss. First, we run an EfficientNet-B0 trained on ImageNet[69]. It 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. Image Classification 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). 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. 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. 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. For smaller models, we set the batch size of unlabeled images to be the same as the batch size of labeled images. to use Codespaces. . If nothing happens, download GitHub Desktop and try again. 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. It can be seen that masks are useful in improving classification performance. 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. 27.8 to 16.1. The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. The main use case of knowledge distillation is model compression by making the student model smaller. First, a teacher model is trained in a supervised fashion. The main difference between our work and these works is that they directly optimize adversarial robustness on unlabeled data, whereas we show that self-training with Noisy Student improves robustness greatly even without directly optimizing robustness. Specifically, we train the student model for 350 epochs for models larger than EfficientNet-B4, including EfficientNet-L0, L1 and L2 and train the student model for 700 epochs for smaller models. 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. (or is it just me), Smithsonian Privacy Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. (2) With out-of-domain unlabeled images, hard pseudo labels can hurt the performance while soft pseudo labels leads to robust performance. 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 Self-training with Noisy Student. We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. over the JFT dataset to predict a label for each image. Chowdhury et al. The results are shown in Figure 4 with the following observations: (1) Soft pseudo labels and hard pseudo labels can both lead to great improvements with in-domain unlabeled images i.e., high-confidence images. Train a larger classifier on the combined set, adding noise (noisy student). In terms of methodology, As noise injection methods are not used in the student model, and the student model was also small, it is more difficult to make the student better than teacher. 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. Learn more. Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. Then, that teacher is used to label the unlabeled data. Algorithm1 gives an overview of self-training with Noisy Student (or Noisy Student in short). We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. In contrast, the predictions of the model with Noisy Student remain quite stable. 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. 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. This model investigates a new method. and surprising gains on robustness and adversarial benchmarks. Self-Training Noisy Student " " Self-Training . Please 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. - : self-training_with_noisy_student_improves_imagenet_classification In all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. 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. 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. Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. We find that using a batch size of 512, 1024, and 2048 leads to the same performance. . Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. Test images on ImageNet-P underwent different scales of perturbations. 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. We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. In other words, the student is forced to mimic a more powerful ensemble model. For labeled images, we use a batch size of 2048 by default and reduce the batch size when we could not fit the model into the memory. For RandAugment, we apply two random operations with the magnitude set to 27. 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. supervised model from 97.9% accuracy to 98.6% accuracy. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. 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.
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