We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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. 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]. Figure 1(c) shows images from ImageNet-P and the corresponding predictions. This way, the pseudo labels are as good as possible, and the noised student is forced to learn harder from the pseudo labels. Self-training with Noisy Student improves ImageNet classificationCVPR2020, Codehttps://github.com/google-research/noisystudent, Self-training, 1, 2Self-training, Self-trainingGoogleNoisy Student, Noisy Studentstudent modeldropout, stochastic depth andaugmentationteacher modelNoisy Noisy Student, Noisy Student, 1, JFT3ImageNetEfficientNet-B00.3130K130K, EfficientNetbaseline modelsEfficientNetresnet, EfficientNet-B7EfficientNet-L0L1L2, batchsize = 2048 51210242048EfficientNet-B4EfficientNet-L0l1L2350epoch700epoch, 2EfficientNet-B7EfficientNet-L0, 3EfficientNet-L0EfficientNet-L1L0, 4EfficientNet-L1EfficientNet-L2, student modelNoisy, noisystudent modelteacher modelNoisy, Noisy, Self-trainingaugmentationdropoutstochastic depth, Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores., 12/self-training-with-noisy-student-f33640edbab2, EfficientNet-L0EfficientNet-B7B7, EfficientNet-L1EfficientNet-L0, EfficientNetsEfficientNet-L1EfficientNet-L2EfficientNet-L2EfficientNet-B75. Add a 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. Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer . 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. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). Work fast with our official CLI. . 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. 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. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (teacher). , have shown that computer vision models lack robustness. Are you sure you want to create this branch? We iterate this process by putting back the student as the teacher. 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. A tag already exists with the provided branch name. Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and w Summary of key results compared to previous state-of-the-art models. Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. Noisy Students performance improves with more unlabeled data. 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. It can be seen that masks are useful in improving classification performance. The most interesting image is shown on the right of the first row. Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a We use our best model Noisy Student with EfficientNet-L2 to teach student models with sizes ranging from EfficientNet-B0 to EfficientNet-B7. 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. Use a model to predict pseudo-labels on the filtered data: This is not an officially supported Google product. Self-Training Noisy Student " " Self-Training . unlabeled images. IEEE Transactions on Pattern Analysis and Machine Intelligence. Figure 1(b) shows images from ImageNet-C and the corresponding predictions. Yalniz et al. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. We then select images that have confidence of the label higher than 0.3. Figure 1(a) shows example images from ImageNet-A and the predictions of our models. To achieve this result, we first train an EfficientNet model on labeled 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. First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. 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. Agreement NNX16AC86A, Is ADS down? These significant gains in robustness in ImageNet-C and ImageNet-P are surprising because our models were not deliberately optimizing for robustness (e.g., via data augmentation). To achieve strong results on ImageNet, the student model also needs to be large, typically larger than common vision models, so that it can leverage a large number of unlabeled images. Please refer to [24] for details about mFR and AlexNets flip probability. mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline. 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. 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. Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. Are labels required for improving adversarial robustness? Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. On robustness test sets, it improves ImageNet-A top . To date (2020) we will introduce "Noisy Student Training", which is a state-of-the-art model.The idea is to extend self-training and Distillation, a paper that shows that by adding three noises and distilling multiple times, the student model will have better generalization performance than the teacher model. 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. 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). possible. Hence we use soft pseudo labels for our experiments unless otherwise specified. Noisy Student improves adversarial robustness against an FGSM attack though the model is not optimized for adversarial robustness. https://arxiv.org/abs/1911.04252. 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. Computer Science - Computer Vision and Pattern Recognition. Next, a larger student model is trained on the combination of all data and achieves better performance than the teacher by itself.OUTLINE:0:00 - Intro \u0026 Overview1:05 - Semi-Supervised \u0026 Transfer Learning5:45 - Self-Training \u0026 Knowledge Distillation10:00 - Noisy Student Algorithm Overview20:20 - Noise Methods22:30 - Dataset Balancing25:20 - Results30:15 - Perturbation Robustness34:35 - Ablation Studies39:30 - Conclusion \u0026 CommentsPaper: https://arxiv.org/abs/1911.04252Code: https://github.com/google-research/noisystudentModels: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnetAbstract: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. On . As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. 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. Flip probability is the probability that the model changes top-1 prediction for different perturbations. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. 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. 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. [^reference-9] [^reference-10] A critical insight was to . 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. Edit social preview. For example, without Noisy Student, the model predicts bullfrog for the image shown on the left of the second row, which might be resulted from the black lotus leaf on the water. This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . student is forced to learn harder from the pseudo labels. 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. Self-training with noisy student improves imagenet classification. We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. Unlike previous studies in semi-supervised learning that use in-domain unlabeled data (e.g, ., CIFAR-10 images as unlabeled data for a small CIFAR-10 training set), to improve ImageNet, we must use out-of-domain unlabeled data. As shown in Figure 1, Noisy Student leads to a consistent improvement of around 0.8% for all model sizes. While removing noise leads to a much lower training loss for labeled images, we observe that, for unlabeled images, removing noise leads to a smaller drop in training loss. Hence the total number of images that we use for training a student model is 130M (with some duplicated images). 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. "Self-training with Noisy Student improves ImageNet classification" pytorch implementation. If nothing happens, download GitHub Desktop and try again. The abundance of data on the internet is vast. By showing the models only labeled images, we limit ourselves from making use of unlabeled images available in much larger quantities to improve accuracy and robustness of state-of-the-art models. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 10687-10698). We present a simple self-training method that achieves 87.4 We obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. 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. About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. [50] used knowledge distillation on unlabeled data to teach a small student model for speech recognition. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. (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. 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 pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). (or is it just me), Smithsonian Privacy Copyright and all rights therein are retained by authors or by other copyright holders.