As we use soft targets, our work is also related to methods in Knowledge Distillation[7, 3, 26, 16]. EfficientNet-L1 approximately doubles the training time of EfficientNet-L0. We sample 1.3M images in confidence intervals. [2] show that Self-Training is superior to Pre-training with ImageNet Supervised Learning on a few Computer . Please refer to [24] for details about mCE and AlexNets error rate. 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. In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. 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. 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. 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 Code is available at this https URL.Authors: Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. 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We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. CLIP: Connecting text and images - OpenAI . Hence the total number of images that we use for training a student model is 130M (with some duplicated images). In other words, the student is forced to mimic a more powerful ensemble model. Self-Training With Noisy Student Improves ImageNet Classification 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. [68, 24, 55, 22]. 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. Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. Note that these adversarial robustness results are not directly comparable to prior works since we use a large input resolution of 800x800 and adversarial vulnerability can scale with the input dimension[17, 20, 19, 61]. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. We improved it by adding noise to the student to learn beyond the teachers knowledge. We verify that this is not the case when we use 130M unlabeled images since the model does not overfit the unlabeled set from the training loss. We iterate this process by putting back the student as the teacher. This work systematically benchmark state-of-the-art methods that use unlabeled data, including domain-invariant, self-training, and self-supervised methods, and shows that their success on WILDS is limited. We use the labeled images to train a teacher model using the standard cross entropy loss. 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. Train a larger classifier on the combined set, adding noise (noisy student). In our implementation, labeled images and unlabeled images are concatenated together and we compute the average cross entropy loss. 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. 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. ImageNet . In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. combination of labeled and pseudo labeled images. Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. 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. Use, Smithsonian on ImageNet, which is 1.0 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. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). Imaging, 39 (11) (2020), pp. You signed in with another tab or window. FixMatch-LS: Semi-supervised skin lesion classification with label Self-training with noisy student improves imagenet classification. In Noisy Student, we combine these two steps into one because it simplifies the algorithm and leads to better performance in our preliminary experiments. Self-training Their noise model is video specific and not relevant for image classification. On ImageNet-C, it reduces mean corruption error (mCE) from 45.7 to 31.2. Noisy Student leads to significant improvements across all model sizes for EfficientNet. Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled data. Are labels required for improving adversarial robustness? [50] used knowledge distillation on unlabeled data to teach a small student model for speech recognition. 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). Using Noisy Student (EfficientNet-L2) as the teacher leads to another 0.8% improvement on top of the improved results. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. However, manually annotating organs from CT scans is time . Papers With Code is a free resource with all data licensed under. PDF Self-Training with Noisy Student Improves ImageNet Classification Lastly, we apply the recently proposed technique to fix train-test resolution discrepancy[71] for EfficientNet-L0, L1 and L2. Distillation Survey : Noisy Student | 9to5Tutorial The architectures for the student and teacher models can be the same or different. Figure 1(a) shows example images from ImageNet-A and the predictions of our models. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. 3429-3440. . Astrophysical Observatory. Work fast with our official CLI. This material is presented to ensure timely dissemination of scholarly and technical work. In particular, we set the survival probability in stochastic depth to 0.8 for the final layer and follow the linear decay rule for other layers. unlabeled images , . Self-training with Noisy Student improves ImageNet 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 . 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. 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 sign in We thank the Google Brain team, Zihang Dai, Jeff Dean, Hieu Pham, Colin Raffel, Ilya Sutskever and Mingxing Tan for insightful discussions, Cihang Xie for robustness evaluation, Guokun Lai, Jiquan Ngiam, Jiateng Xie and Adams Wei Yu for feedbacks on the draft, Yanping Huang and Sameer Kumar for improving TPU implementation, Ekin Dogus Cubuk and Barret Zoph for help with RandAugment, Yanan Bao, Zheyun Feng and Daiyi Peng for help with the JFT dataset, Olga Wichrowska and Ola Spyra for help with infrastructure. 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. Iterative training is not used here for simplicity. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. Noisy Student can still improve the accuracy to 1.6%. 10687-10698 Abstract On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . The accuracy is improved by about 10% in most settings. Noisy Student improves adversarial robustness against an FGSM attack though the model is not optimized for adversarial robustness. We use EfficientNet-B0 as both the teacher model and the student model and compare using Noisy Student with soft pseudo labels and hard pseudo labels. 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. Zoph et al. For each class, we select at most 130K images that have the highest confidence. We iterate this process by putting back the student as the teacher. Soft pseudo labels lead to better performance for low confidence data. The top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled 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). Especially unlabeled images are plentiful and can be collected with ease. [^reference-9] [^reference-10] A critical insight was to . A new scaling method is proposed that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient and is demonstrated the effectiveness of this method on scaling up MobileNets and ResNet. Self-training with Noisy Student. Their purpose is different from ours: to adapt a teacher model on one domain to another. 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. The width. 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. Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. The performance consistently drops with noise function removed. We determine number of training steps and the learning rate schedule by the batch size for labeled images. Conclusion, Abstract , ImageNet , web-scale extra labeled images weakly labeled Instagram images weakly-supervised learning . (or is it just me), Smithsonian Privacy About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . 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. Train a classifier on labeled data (teacher). 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. It implements SemiSupervised Learning with Noise to create an Image Classification. Self-training with Noisy Student improves ImageNet classification . We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. Please refer to [24] for details about mFR and AlexNets flip probability. 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. A semi-supervised segmentation network based on noisy student learning 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. We use a resolution of 800x800 in this experiment. Self-Training for Natural Language Understanding! Computer Science - Computer Vision and Pattern Recognition. 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 work introduces two challenging datasets that reliably cause machine learning model performance to substantially degrade and curates an adversarial out-of-distribution detection dataset called IMAGENET-O, which is the first out- of-dist distribution detection dataset created for ImageNet models. et al. If you get a better model, you can use the model to predict pseudo-labels on the filtered data. We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. Significantly, after using the masks generated by student-SN, the classification performance improved by 0.9 of AC, 0.7 of SE, and 0.9 of AUC. Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. Train a larger classifier on the combined set, adding noise (noisy student). Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. 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. Using self-training with Noisy Student, together with 300M unlabeled images, we improve EfficientNets[69] ImageNet top-1 accuracy to 87.4%. The model with Noisy Student can successfully predict the correct labels of these highly difficult images. The proposed use of distillation to only handle easy instances allows for a more aggressive trade-off in the student size, thereby reducing the amortized cost of inference and achieving better accuracy than standard distillation. Use a model to predict pseudo-labels on the filtered data: This is not an officially supported Google product. 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. 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. It can be seen that masks are useful in improving classification performance. Hence, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels. Since a teacher models confidence on an image can be a good indicator of whether it is an out-of-domain image, we consider the high-confidence images as in-domain images and the low-confidence images as out-of-domain images. Self-Training : Noisy Student : Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. 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. 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]. The abundance of data on the internet is vast.
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