self training with noisy student improves imagenet classificationguess ethnicity by photo quiz

GitHub - google-research/noisystudent: Code for Noisy Student Training A common workaround is to use entropy minimization or ramp up the consistency loss. 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. self-mentoring outperforms data augmentation and self training. Finally, in the above, we say that the pseudo labels can be soft or hard. Self-training with noisy student improves imagenet classification. Are you sure you want to create this branch? Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. Specifically, as all classes in ImageNet have a similar number of labeled images, we also need to balance the number of unlabeled images for each class. Their purpose is different from ours: to adapt a teacher model on one domain to another. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model 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. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images In Noisy Student, we combine these two steps into one because it simplifies the algorithm and leads to better performance in our preliminary experiments. 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. Self-training with Noisy Student improves ImageNet classification First, we run an EfficientNet-B0 trained on ImageNet[69]. Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. The model with Noisy Student can successfully predict the correct labels of these highly difficult images. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. 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. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from The algorithm is basically self-training, a method in semi-supervised learning (. 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. Self-training with Noisy Student improves ImageNet classification We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. 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. On ImageNet-C, it reduces mean corruption error (mCE) from 45.7 to 31.2. Classification of Socio-Political Event Data, SLADE: A Self-Training Framework For Distance Metric Learning, Self-Training with Differentiable Teacher, https://github.com/hendrycks/natural-adv-examples/blob/master/eval.py. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. 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. 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. Train a classifier on labeled data (teacher). This material is presented to ensure timely dissemination of scholarly and technical work. We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. 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. 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]. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. to noise the 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. The abundance of data on the internet is vast. combination of labeled and pseudo labeled images. 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. Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. We used the version from [47], which filtered the validation set of ImageNet. Self-training with Noisy Student improves ImageNet classification IEEE Trans. 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. . 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. Do imagenet classifiers generalize to imagenet? The top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. In addition to improving state-of-the-art results, we conduct additional experiments to verify if Noisy Student can benefit other EfficienetNet models. Use Git or checkout with SVN using the web URL. In all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. Works based on pseudo label[37, 31, 60, 1] are similar to self-training, but also suffers the same problem with consistency training, since it relies on a model being trained instead of a converged model with high accuracy to generate pseudo labels. This invariance constraint reduces the degrees of freedom in the model. 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. As shown in Figure 1, Noisy Student leads to a consistent improvement of around 0.8% for all model sizes. 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. We iterate this process by Work fast with our official CLI. EfficientNet-L1 approximately doubles the training time of EfficientNet-L0. Chowdhury et al. 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. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. Use Git or checkout with SVN using the web URL. CLIP: Connecting text and images - OpenAI 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. The architectures for the student and teacher models can be the same or different. Self-training with Noisy Student improves ImageNet classification IEEE Transactions on Pattern Analysis and Machine Intelligence. 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. Semi-supervised medical image classification with relation-driven self-ensembling model. Then, that teacher is used to label the unlabeled data. The most interesting image is shown on the right of the first row. 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. 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. Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. After using the masks generated by teacher-SN, the classification performance improved by 0.2 of AC, 1.2 of SP, and 0.7 of AUC. 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. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. 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. 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. We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. 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. 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. Noise Self-training with Noisy Student 1. We sample 1.3M images in confidence intervals. Med. Self-training with Noisy Student improves ImageNet classification 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. As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. . Edit social preview. PDF Self-Training with Noisy Student Improves ImageNet Classification Add a In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. A new scaling method is proposed that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient and is demonstrated the effectiveness of this method on scaling up MobileNets and ResNet. Further, Noisy Student outperforms the state-of-the-art accuracy of 86.4% by FixRes ResNeXt-101 WSL[44, 71] that requires 3.5 Billion Instagram images labeled with tags. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. 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. supervised model from 97.9% accuracy to 98.6% accuracy. 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. 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. But training robust supervised learning models is requires this step. In terms of methodology, CVPR 2020 Open Access Repository We iterate this process by putting back the student as the teacher. Finally, for classes that have less than 130K images, we duplicate some images at random so that each class can have 130K images. 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. The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . Our main results are shown in Table1. to use Codespaces. For more information about the large architectures, please refer to Table7 in Appendix A.1. Self-training with Noisy Student improves ImageNet classification. Noisy Student Explained | Papers With Code We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative

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