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. 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. 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. We apply RandAugment to all EfficientNet baselines, leading to more competitive baselines. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images The architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. Their purpose is different from ours: to adapt a teacher model on one domain to another. Agreement NNX16AC86A, Is ADS down? Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. 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. Noisy Students performance improves with more unlabeled data. 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. We apply dropout to the final classification layer with a dropout rate of 0.5. to noise the student. [2] show that Self-Training is superior to Pre-training with ImageNet Supervised Learning on a few Computer . 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. ImageNet-A test set[25] consists of difficult images that cause significant drops in accuracy to state-of-the-art models. Please Self-training with noisy student improves imagenet classification. 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. Amongst other components, Noisy Student implements Self-Training in the context of Semi-Supervised Learning. We iterate this process by putting back the student as the teacher. Here we show the evidence in Table 6, noise such as stochastic depth, dropout and data augmentation plays an important role in enabling the student model to perform better than the teacher. In terms of methodology, The top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. 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 . Notice, Smithsonian Terms of However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. Using Noisy Student (EfficientNet-L2) as the teacher leads to another 0.8% improvement on top of the improved results. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. . Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. Stochastic Depth is a simple yet ingenious idea to add noise to the model by bypassing the transformations through skip connections. , have shown that computer vision models lack robustness. 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. 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. Train a classifier on labeled data (teacher). unlabeled images. Z. Yalniz, H. Jegou, K. Chen, M. Paluri, and D. Mahajan, Billion-scale semi-supervised learning for image classification, Z. Yang, W. W. Cohen, and R. Salakhutdinov, Revisiting semi-supervised learning with graph embeddings, Z. Yang, J. Hu, R. Salakhutdinov, and W. W. Cohen, Semi-supervised qa with generative domain-adaptive nets, Unsupervised word sense disambiguation rivaling supervised methods, 33rd annual meeting of the association for computational linguistics, R. Zhai, T. Cai, D. He, C. Dan, K. He, J. Hopcroft, and L. Wang, Adversarially robust generalization just requires more unlabeled data, X. Zhai, A. Oliver, A. Kolesnikov, and L. Beyer, Proceedings of the IEEE international conference on computer vision, Making convolutional networks shift-invariant again, X. Zhang, Z. Li, C. Change Loy, and D. Lin, Polynet: a pursuit of structural diversity in very deep networks, X. Zhu, Z. Ghahramani, and J. D. Lafferty, Semi-supervised learning using gaussian fields and harmonic functions, Proceedings of the 20th International conference on Machine learning (ICML-03), Semi-supervised learning literature survey, University of Wisconsin-Madison Department of Computer Sciences, B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le, Learning transferable architectures for scalable image recognition, Architecture specifications for EfficientNet used in the paper. 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. 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. 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. Noisy Student improves adversarial robustness against an FGSM attack though the model is not optimized for adversarial robustness. This way, the pseudo labels are as good as possible, and the noised student is forced to learn harder from the pseudo labels. supervised model from 97.9% accuracy to 98.6% accuracy. You signed in with another tab or window. all 12, Image Classification Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. 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 Training seeks to improve on self-training and distillation in two ways. The baseline model achieves an accuracy of 83.2. In the above experiments, iterative training was used to optimize the accuracy of EfficientNet-L2 but here we skip it as it is difficult to use iterative training for many experiments. 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. 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. The main use case of knowledge distillation is model compression by making the student model smaller. An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. 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. But during the learning of the student, we inject noise such as data First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. We use our best model Noisy Student with EfficientNet-L2 to teach student models with sizes ranging from EfficientNet-B0 to EfficientNet-B7. Self-Training With Noisy Student Improves ImageNet Classification. For smaller models, we set the batch size of unlabeled images to be the same as the batch size of labeled images. This invariance constraint reduces the degrees of freedom in the model. Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. The abundance of data on the internet is vast. These CVPR 2020 papers are the Open Access versions, provided by the. 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. on ImageNet ReaL. 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. Test images on ImageNet-P underwent different scales of perturbations. We then perform data filtering and balancing on this corpus. Addressing the lack of robustness has become an important research direction in machine learning and computer vision in recent years. 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. As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. Chowdhury et al. 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. We will then show our results on ImageNet and compare them with state-of-the-art models. 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. Hence we use soft pseudo labels for our experiments unless otherwise specified. Iterative training is not used here for simplicity. 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. However an important requirement for Noisy Student to work well is that the student model needs to be sufficiently large to fit more data (labeled and pseudo labeled). We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. 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. They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. Our main results are shown in Table1. 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. 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. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet.