self training with noisy student improves imagenet classification

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. Papers With Code is a free resource with all data licensed under. We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. 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. 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. . Work fast with our official CLI. Notice, Smithsonian Terms of We iterate this process by Image Classification One might argue that the improvements from using noise can be resulted from preventing overfitting the pseudo labels on the unlabeled images. 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. 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. 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. 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. Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. Use Git or checkout with SVN using the web URL. 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 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 used the version from [47], which filtered the validation set of ImageNet. Please On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . We find that Noisy Student is better with an additional trick: data balancing. The performance drops when we further reduce it. 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. augmentation, dropout, stochastic depth to the student so that the noised As can be seen, our model with Noisy Student makes correct and consistent predictions as images undergone different perturbations while the model without Noisy Student flips predictions frequently. You signed in with another tab or window. 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. It is found that training and scaling strategies may matter more than architectural changes, and further, that the resulting ResNets match recent state-of-the-art models. 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. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. Zoph et al. 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. The most interesting image is shown on the right of the first row. We do not tune these hyperparameters extensively since our method is highly robust to them. By clicking accept or continuing to use the site, you agree to the terms outlined in our. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. Learn more. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. Do imagenet classifiers generalize to imagenet? Addressing the lack of robustness has become an important research direction in machine learning and computer vision in recent years. Infer labels on a much larger unlabeled dataset. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. unlabeled 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. Parthasarathi et al. We iterate this process by putting back the student as the teacher. Different kinds of noise, however, may have different effects. As we use soft targets, our work is also related to methods in Knowledge Distillation[7, 3, 26, 16]. 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. If nothing happens, download Xcode and try again. Especially unlabeled images are plentiful and can be collected with ease. 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. For classes where we have too many images, we take the images with the highest confidence. . IEEE Trans. The ONCE (One millioN sCenEs) dataset for 3D object detection in the autonomous driving scenario is introduced and a benchmark is provided in which a variety of self-supervised and semi- supervised methods on the ONCE dataset are evaluated. ImageNet-A top-1 accuracy from 16.6 [^reference-9] [^reference-10] A critical insight was to . 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. Figure 1(c) shows images from ImageNet-P and the corresponding predictions. Noise Self-training with Noisy Student 1. Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. In other words, small changes in the input image can cause large changes to the predictions. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. However, in the case with 130M unlabeled images, with noise function removed, the performance is still improved to 84.3% from 84.0% when compared to the supervised baseline. Imaging, 39 (11) (2020), pp. Hence the total number of images that we use for training a student model is 130M (with some duplicated images). 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 with Noisy Student improves ImageNet classification. 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. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). A novel random matrix theory based damping learner for second order optimisers inspired by linear shrinkage estimation is developed, and it is demonstrated that the derived method works well with adaptive gradient methods such as Adam. But training robust supervised learning models is requires this step. The main difference between our work and prior works is that we identify the importance of noise, and aggressively inject noise to make the student better. 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. 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. We present a simple self-training method that achieves 87.4 ImageNet-A test set[25] consists of difficult images that cause significant drops in accuracy to state-of-the-art models. [50] used knowledge distillation on unlabeled data to teach a small student model for speech recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. We determine number of training steps and the learning rate schedule by the batch size for labeled images. 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. (or is it just me), Smithsonian Privacy We will then show our results on ImageNet and compare them with state-of-the-art models. 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 commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Are you sure you want to create this branch? First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. on ImageNet ReaL 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. Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled data. Test images on ImageNet-P underwent different scales of perturbations. Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. We iterate this process by putting back the student as the teacher. https://arxiv.org/abs/1911.04252. 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. We use EfficientNet-B4 as both the teacher and the student. 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]. Noisy Student Training is a semi-supervised learning approach. We find that using a batch size of 512, 1024, and 2048 leads to the same performance. 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. However, manually annotating organs from CT scans is time . 10687-10698). Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. to use Codespaces. 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. If you get a better model, you can use the model to predict pseudo-labels on the filtered data. Here we study how to effectively use out-of-domain data. 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. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. . Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. 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]. "Self-training with Noisy Student improves ImageNet classification" pytorch implementation. Models are available at this https URL. Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. 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. Hence we use soft pseudo labels for our experiments unless otherwise specified. 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.

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