self training with noisy student improves imagenet classificationheart 1980 tour dates
possible. 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. Their purpose is different from ours: to adapt a teacher model on one domain to another. to use Codespaces. 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. Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. In other words, small changes in the input image can cause large changes to the predictions. 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 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. 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. 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. 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. Infer labels on a much larger unlabeled dataset. Computer Science - Computer Vision and Pattern Recognition. 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. Their main goal is to find a small and fast model for deployment. It is experimentally validated that, for a target test resolution, using a lower train resolution offers better classification at test time, and a simple yet effective and efficient strategy to optimize the classifier performance when the train and test resolutions differ is proposed. We sample 1.3M images in confidence intervals. But during the learning of the student, we inject noise such as data Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical Chowdhury et al. [2] show that Self-Training is superior to Pre-training with ImageNet Supervised Learning on a few Computer . 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. We used the version from [47], which filtered the validation set of ImageNet. Soft pseudo labels lead to better performance for low confidence data. and surprising gains on robustness and adversarial benchmarks. Noisy Student can still improve the accuracy to 1.6%. to noise the student. It can be seen that masks are useful in improving classification performance. The model with Noisy Student can successfully predict the correct labels of these highly difficult images. Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. 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. 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. Notice, Smithsonian Terms of It implements SemiSupervised Learning with Noise to create an Image Classification. They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. 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 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. The comparison is shown in Table 9. Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. Figure 1(b) shows images from ImageNet-C and the corresponding predictions. 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. 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. Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled data. 27.8 to 16.1. This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. 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. Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. For smaller models, we set the batch size of unlabeled images to be the same as the batch size of labeled 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. We improved it by adding noise to the student to learn beyond the teachers knowledge. The performance drops when we further reduce it. Figure 1(c) shows images from ImageNet-P and the corresponding predictions. (2) With out-of-domain unlabeled images, hard pseudo labels can hurt the performance while soft pseudo labels leads to robust performance. 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. Stochastic Depth is a simple yet ingenious idea to add noise to the model by bypassing the transformations through skip connections. Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. If you get a better model, you can use the model to predict pseudo-labels on the filtered data. We start with the 130M unlabeled images and gradually reduce the number of images. Self-training with Noisy Student. Astrophysical Observatory. 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. . Aerial Images Change Detection, Multi-Task Self-Training for Learning General Representations, Self-Training Vision Language BERTs with a Unified Conditional Model, 1Cademy @ Causal News Corpus 2022: Leveraging Self-Training in Causality The results also confirm that vision models can benefit from Noisy Student even without iterative training. We apply dropout to the final classification layer with a dropout rate of 0.5. sign in 10687-10698 Abstract Noisy Student leads to significant improvements across all model sizes for EfficientNet. [68, 24, 55, 22]. Finally, for classes that have less than 130K images, we duplicate some images at random so that each class can have 130K images. Learn more. Please refer to [24] for details about mCE and AlexNets error rate. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. 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. 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). 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. Please We will then show our results on ImageNet and compare them with state-of-the-art models. labels, the teacher is not noised so that the pseudo labels are as good as 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. Especially unlabeled images are plentiful and can be collected with ease. Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. Image Classification We use EfficientNets[69] as our baseline models because they provide better capacity for more data. Self-Training With Noisy Student Improves ImageNet Classification. 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. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. You signed in with another tab or window. We duplicate images in classes where there are not enough images. 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. We also study the effects of using different amounts of unlabeled data. Here we use unlabeled images to improve the state-of-the-art ImageNet accuracy and show that the accuracy gain has an outsized impact on robustness. Self-training This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . We iterate this process by The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. to use Codespaces. Work fast with our official CLI. Then we finetune the model with a larger resolution for 1.5 epochs on unaugmented labeled images. Train a classifier on labeled data (teacher). Code is available at https://github.com/google-research/noisystudent. The hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. We first report the validation set accuracy on the ImageNet 2012 ILSVRC challenge prediction task as commonly done in literature[35, 66, 23, 69] (see also [55]). 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 all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. If nothing happens, download GitHub Desktop and try again. 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. Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. Med. 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. Work fast with our official CLI. 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]. The score is normalized by AlexNets error rate so that corruptions with different difficulties lead to scores of a similar scale. To noise the student, we use dropout[63], data augmentation[14] and stochastic depth[29] during its training. Self-training with Noisy Student improves ImageNet classification Original paper: https://arxiv.org/pdf/1911.04252.pdf Authors: Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le HOYA012 Introduction EfficientNet ImageNet SOTA EfficientNet We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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. 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 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. Here we study how to effectively use out-of-domain data. Hence, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels. In this section, we study the importance of noise and the effect of several noise methods used in our model. We vary the model size from EfficientNet-B0 to EfficientNet-B7[69] and use the same model as both the teacher and the student. 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. Self-Training Noisy Student " " Self-Training . There was a problem preparing your codespace, please try again. We obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. In our implementation, labeled images and unlabeled images are concatenated together and we compute the average cross entropy loss. Figure 1(a) shows example images from ImageNet-A and the predictions of our models. We present a simple self-training method that achieves 87.4 Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. The top-1 accuracy is simply the average top-1 accuracy for all corruptions and all severity degrees. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative We iterate this process by putting back the student as the teacher. 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. For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. 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. Noisy Student improves adversarial robustness against an FGSM attack though the model is not optimized for adversarial robustness. One might argue that the improvements from using noise can be resulted from preventing overfitting the pseudo labels on the unlabeled images. We find that using a batch size of 512, 1024, and 2048 leads to the same performance. Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. 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. The pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). We then train a larger EfficientNet as a student model on the You can also use the colab script noisystudent_svhn.ipynb to try the method on free Colab GPUs. Parthasarathi et al. 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. Copyright and all rights therein are retained by authors or by other copyright holders. 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 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. 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. The architectures for the student and teacher models can be the same or different. Finally, in the above, we say that the pseudo labels can be soft or hard. We conduct experiments on ImageNet 2012 ILSVRC challenge prediction task since it has been considered one of the most heavily benchmarked datasets in computer vision and that improvements on ImageNet transfer to other datasets. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2.
