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

https://arxiv.org/abs/1911.04252. In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. There was a problem preparing your codespace, please try again. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. Zoph et al. The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet and surprising gains on robustness and adversarial benchmarks. On ImageNet-C, it reduces mean corruption error (mCE) from 45.7 to 31.2. 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. 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. . 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. labels, the teacher is not noised so that the pseudo labels are as good as 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]. This shows that it is helpful to train a large model with high accuracy using Noisy Student when small models are needed for deployment. Le. Noisy StudentImageNetEfficientNet-L2state-of-the-art. Here we study if it is possible to improve performance on small models by using a larger teacher model, since small models are useful when there are constraints for model size and latency in real-world applications. It implements SemiSupervised Learning with Noise to create an Image Classification. In contrast, the predictions of the model with Noisy Student remain quite stable. We iterate this process by putting back the student as the teacher. We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. Please Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. Papers With Code is a free resource with all data licensed under. However, manually annotating organs from CT scans is time . Using self-training with Noisy Student, together with 300M unlabeled images, we improve EfficientNets[69] ImageNet top-1 accuracy to 87.4%. An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. 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. 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. The performance drops when we further reduce it. This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. 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. 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. Hence the total number of images that we use for training a student model is 130M (with some duplicated images). There was a problem preparing your codespace, please try again. Due to duplications, there are only 81M unique images among these 130M images. 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. 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. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. 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. Abdominal organ segmentation is very important for clinical applications. In terms of methodology, We also list EfficientNet-B7 as a reference. Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. 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. We sample 1.3M images in confidence intervals. Flip probability is the probability that the model changes top-1 prediction for different perturbations. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. The accuracy is improved by about 10% in most settings. 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. Noisy Student (B7, L2) means to use EfficientNet-B7 as the student and use our best model with 87.4% accuracy as the teacher model. Selected images from robustness benchmarks ImageNet-A, C and P. Test images from ImageNet-C underwent artificial transformations (also known as common corruptions) that cannot be found on the ImageNet training set. 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. For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. (using extra training data). During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. Learn more. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model 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. to use Codespaces. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. 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. The performance consistently drops with noise function removed. The top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (teacher). Finally, for classes that have less than 130K images, we duplicate some images at random so that each class can have 130K images. (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. Code for Noisy Student Training. We then select images that have confidence of the label higher than 0.3. 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]. We find that using a batch size of 512, 1024, and 2048 leads to the same performance. 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. mCE (mean corruption error) is the weighted average of error rate on different corruptions, with AlexNets error rate as a baseline. 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 particular, we first perform normal training with a smaller resolution for 350 epochs. 27.8 to 16.1. You can also use the colab script noisystudent_svhn.ipynb to try the method on free Colab GPUs. Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. Self-Training Noisy Student " " Self-Training . As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. We improved it by adding noise to the student to learn beyond the teachers knowledge. This invariance constraint reduces the degrees of freedom in the model. For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. You signed in with another tab or window. But during the learning of the student, we inject noise such as data Are labels required for improving adversarial robustness? The architectures for the student and teacher models can be the same or different. In addition to improving state-of-the-art results, we conduct additional experiments to verify if Noisy Student can benefit other EfficienetNet models. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. 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 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. For classes where we have too many images, we take the images with the highest confidence. Noisy Student leads to significant improvements across all model sizes for EfficientNet. 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. On robustness test sets, it improves As shown in Figure 1, Noisy Student leads to a consistent improvement of around 0.8% for all model sizes. 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. The most interesting image is shown on the right of the first row. The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). Use a model to predict pseudo-labels on the filtered data: This is not an officially supported Google product. As we use soft targets, our work is also related to methods in Knowledge Distillation[7, 3, 26, 16]. As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. [^reference-9] [^reference-10] A critical insight was to . The top-1 accuracy is simply the average top-1 accuracy for all corruptions and all severity degrees. 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. Noisy Student Training seeks to improve on self-training and distillation in two ways. If nothing happens, download GitHub Desktop and try again. 3.5B weakly labeled Instagram images. Please refer to [24] for details about mFR and AlexNets flip probability. The Wilds 2.0 update is presented, which extends 8 of the 10 datasets in the Wilds benchmark of distribution shifts to include curated unlabeled data that would be realistically obtainable in deployment, and systematically benchmark state-of-the-art methods that leverage unlabeling data, including domain-invariant, self-training, and self-supervised methods. 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 use EfficientNet-B4 as both the teacher and the student. We iterate this process by This is probably because it is harder to overfit the large unlabeled dataset. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Different types of. Stochastic Depth is a simple yet ingenious idea to add noise to the model by bypassing the transformations through skip connections. 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. 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. 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. Ranked #14 on This model investigates a new method. We use the same architecture for the teacher and the student and do not perform iterative training. This paper proposes a pipeline, based on a teacher/student paradigm, that leverages a large collection of unlabelled images to improve the performance for a given target architecture, like ResNet-50 or ResNext. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. unlabeled 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. Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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. Hence, EfficientNet-L0 has around the same training speed with EfficientNet-B7 but more parameters that give it a larger capacity. This material is presented to ensure timely dissemination of scholarly and technical work. Instructions on running prediction on unlabeled data, filtering and balancing data and training using the stored predictions. putting back the student as the teacher. We iterate this process by putting back the student as the teacher. 10687-10698 Abstract A tag already exists with the provided branch name. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. . It is expensive and must be done with great care. and surprising gains on robustness and adversarial benchmarks. , have shown that computer vision models lack robustness. Similar to[71], we fix the shallow layers during finetuning. We use the standard augmentation instead of RandAugment in this experiment. Code is available at https://github.com/google-research/noisystudent. task. The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. 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. First, a teacher model is trained in a supervised fashion. Copyright and all rights therein are retained by authors or by other copyright holders. 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. Code for Noisy Student Training. You signed in with another tab or window. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. Stochastic depth is proposed, a training procedure that enables the seemingly contradictory setup to train short networks and use deep networks at test time and reduces training time substantially and improves the test error significantly on almost all data sets that were used for evaluation. ImageNet . Use Git or checkout with SVN using the web URL. We will then show our results on ImageNet and compare them with state-of-the-art models. Then we finetune the model with a larger resolution for 1.5 epochs on unaugmented labeled images. This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . 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. We obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. all 12, Image Classification 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. Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. Summarization_self-training_with_noisy_student_improves_imagenet_classification. Learn more. During the generation of the pseudo However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. 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. Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. Notice, Smithsonian Terms of 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. E. Arazo, D. Ortego, P. Albert, N. E. OConnor, and K. McGuinness, Pseudo-labeling and confirmation bias in deep semi-supervised learning, B. Athiwaratkun, M. Finzi, P. Izmailov, and A. G. Wilson, There are many consistent explanations of unlabeled data: why you should average, International Conference on Learning Representations, Advances in Neural Information Processing Systems, D. Berthelot, N. Carlini, I. Goodfellow, N. Papernot, A. Oliver, and C. Raffel, MixMatch: a holistic approach to semi-supervised learning, Combining labeled and unlabeled data with co-training, C. Bucilu, R. Caruana, and A. Niculescu-Mizil, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, Y. Carmon, A. Raghunathan, L. Schmidt, P. Liang, and J. C. Duchi, Unlabeled data improves adversarial robustness, Semi-supervised learning (chapelle, o. et al., eds. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. IEEE Trans. We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. Our main results are shown in Table1. On . 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]. We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. Especially unlabeled images are plentiful and can be collected with ease. 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. Train a classifier on labeled data (teacher). Chowdhury et al. mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline. Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores. Edit social preview. augmentation, dropout, stochastic depth to the student so that the noised By clicking accept or continuing to use the site, you agree to the terms outlined in our. Use, Smithsonian EfficientNet-L1 approximately doubles the training time of EfficientNet-L0. Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. This article demonstrates the first tool based on a convolutional Unet++ encoderdecoder architecture for the semantic segmentation of in vitro angiogenesis simulation images followed by the resulting mask postprocessing for data analysis by experts. 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. Figure 1(c) shows images from ImageNet-P and the corresponding predictions. 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. Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. 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. 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. These CVPR 2020 papers are the Open Access versions, provided by the. 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. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage.