self training with noisy student improves imagenet classification

Then, that teacher is used to label the unlabeled data. The results also confirm that vision models can benefit from Noisy Student even without iterative training. Infer labels on a much larger unlabeled dataset. CVPR 2020 Open Access Repository 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. Finally, in the above, we say that the pseudo labels can be soft or hard. The architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled data. This way, the pseudo labels are as good as possible, and the noised student is forced to learn harder from the pseudo labels. When the student model is deliberately noised it is actually trained to be consistent to the more powerful teacher model that is not noised when it generates pseudo labels. 27.8 to 16.1. For smaller models, we set the batch size of unlabeled images to be the same as the batch size of labeled 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. to use Codespaces. 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. Iterative training is not used here for simplicity. The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative (2) With out-of-domain unlabeled images, hard pseudo labels can hurt the performance while soft pseudo labels leads to robust performance. Copyright and all rights therein are retained by authors or by other copyright holders. FixMatch-LS: Semi-supervised skin lesion classification with label 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. . 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. Papers With Code is a free resource with all data licensed under. Ranked #14 on 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. . Learn more. Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and 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. Hence the total number of images that we use for training a student model is 130M (with some duplicated images). Self-training 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. Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. w Summary of key results compared to previous state-of-the-art models. Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. A semi-supervised segmentation network based on noisy student learning As we use soft targets, our work is also related to methods in Knowledge Distillation[7, 3, 26, 16]. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. In this section, we study the importance of noise and the effect of several noise methods used in our model. self-mentoring outperforms data augmentation and self training. These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. For more information about the large architectures, please refer to Table7 in Appendix A.1. 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. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model . We use the labeled images to train a teacher model using the standard cross entropy loss. Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. unlabeled images. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Their framework is highly optimized for videos, e.g., prediction on which frame to use in a video, which is not as general as our work. 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. We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. 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. A common workaround is to use entropy minimization or ramp up the consistency loss. For RandAugment, we apply two random operations with the magnitude set to 27. 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. 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. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. We then select images that have confidence of the label higher than 0.3. 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. As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. [2] show that Self-Training is superior to Pre-training with ImageNet Supervised Learning on a few Computer . 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. We iterate this process by If you get a better model, you can use the model to predict pseudo-labels on the filtered data. [68, 24, 55, 22]. Image Classification Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10687-10698, (2020 . 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. 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. , have shown that computer vision models lack robustness. . Self-training with Noisy Student. You can also use the colab script noisystudent_svhn.ipynb to try the method on free Colab GPUs. 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 both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. First, we run an EfficientNet-B0 trained on ImageNet[69]. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. The main difference between our work and these works is that they directly optimize adversarial robustness on unlabeled data, whereas we show that self-training with Noisy Student improves robustness greatly even without directly optimizing robustness. (or is it just me), Smithsonian Privacy IEEE Trans. We use stochastic depth[29], dropout[63] and RandAugment[14]. Why Self-training with Noisy Students beats SOTA Image classification 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. Please refer to [24] for details about mCE and AlexNets error rate. In this work, we showed that it is possible to use unlabeled images to significantly advance both accuracy and robustness of state-of-the-art ImageNet models. 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. But training robust supervised learning models is requires this step. 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. 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. This is probably because it is harder to overfit the large unlabeled dataset. supervised model from 97.9% accuracy to 98.6% accuracy. We apply dropout to the final classification layer with a dropout rate of 0.5. Addressing the lack of robustness has become an important research direction in machine learning and computer vision in recent years. Semi-supervised medical image classification with relation-driven self-ensembling model. As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. Add a Here we study how to effectively use out-of-domain data. 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. First, a teacher model is trained in a supervised fashion. This work adopts the noisy-student learning method, and adopts 3D nnUNet as the segmentation model during the experiments, since No new U-Net is the state-of-the-art medical image segmentation method and designs task-specific pipelines for different tasks. In the following, we will first describe experiment details to achieve our results. mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline. We do not tune these hyperparameters extensively since our method is highly robust to them. 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. 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 will then show our results on ImageNet and compare them with state-of-the-art models. Amongst other components, Noisy Student implements Self-Training in the context of Semi-Supervised Learning. On robustness test sets, it improves The inputs to the algorithm are both labeled and unlabeled images. The comparison is shown in Table 9. Abdominal organ segmentation is very important for clinical applications. We also list EfficientNet-B7 as a reference. We find that using a batch size of 512, 1024, and 2048 leads to the same performance. on ImageNet ReaL. An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. Please refer to [24] for details about mFR and AlexNets flip probability. The learning rate starts at 0.128 for labeled batch size 2048 and decays by 0.97 every 2.4 epochs if trained for 350 epochs or every 4.8 epochs if trained for 700 epochs. We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. 10687-10698). EfficientNet-L1 approximately doubles the training time of EfficientNet-L0. We iterate this process by putting back the student as the teacher. 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. One might argue that the improvements from using noise can be resulted from preventing overfitting the pseudo labels on the unlabeled images. We use a resolution of 800x800 in this experiment. With Noisy Student, the model correctly predicts dragonfly for the image. A tag already exists with the provided branch name. 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. sign in The model with Noisy Student can successfully predict the correct labels of these highly difficult images. Summarization_self-training_with_noisy_student_improves_imagenet Code is available at https://github.com/google-research/noisystudent. Code is available at https://github.com/google-research/noisystudent. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 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. We iterate this process by putting back the student as the teacher. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 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. In other words, the student is forced to mimic a more powerful ensemble model. We duplicate images in classes where there are not enough 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. 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. Self-Training With Noisy Student Improves ImageNet Classification 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. For labeled images, we use a batch size of 2048 by default and reduce the batch size when we could not fit the model into the memory. 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. 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. Chowdhury et al. Our procedure went as follows. Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. As shown in Figure 1, Noisy Student leads to a consistent improvement of around 0.8% for all model sizes. We iterate this process by putting back the student as the teacher. A number of studies, e.g. Self-Training With Noisy Student Improves ImageNet Classification Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. 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. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. 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]. 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. SelfSelf-training with Noisy Student improves ImageNet classification We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. Self-training with Noisy Student improves ImageNet classification 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. CLIP (Contrastive Language-Image Pre-training) builds on a large body of work on zero-shot transfer, natural language supervision, and multimodal learning.The idea of zero-data learning dates back over a decade [^reference-8] but until recently was mostly studied in computer vision as a way of generalizing to unseen object categories. The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. "Self-training with Noisy Student improves ImageNet classification" pytorch implementation. On ImageNet-C, it reduces mean corruption error (mCE) from 45.7 to 31.2. 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. Edit social preview. This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. The baseline model achieves an accuracy of 83.2. We also study the effects of using different amounts of unlabeled data. To intuitively understand the significant improvements on the three robustness benchmarks, we show several images in Figure2 where the predictions of the standard model are incorrect and the predictions of the Noisy Student model are correct.