2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. A tag already exists with the provided branch name. on ImageNet ReaL. We find that using a batch size of 512, 1024, and 2048 leads to the same performance. The main difference between Data Distillation and our method is that we use the noise to weaken the student, which is the opposite of their approach of strengthening the teacher by ensembling. Amongst other components, Noisy Student implements Self-Training in the context of Semi-Supervised Learning. Ranked #14 on Instructions on running prediction on unlabeled data, filtering and balancing data and training using the stored predictions. These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). 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. 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. Self-Training Noisy Student " " Self-Training . Noisy StudentImageNetEfficientNet-L2state-of-the-art. 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. 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. Apart from self-training, another important line of work in semi-supervised learning[9, 85] is based on consistency training[6, 4, 53, 36, 70, 45, 41, 51, 10, 12, 49, 2, 38, 72, 74, 5, 81]. The performance consistently drops with noise function removed. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. The top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. Test images on ImageNet-P underwent different scales of perturbations. Abdominal organ segmentation is very important for clinical applications. Self-Training With Noisy Student Improves ImageNet Classification @article{Xie2019SelfTrainingWN, title={Self-Training With Noisy Student Improves ImageNet Classification}, author={Qizhe Xie and Eduard H. Hovy and Minh-Thang Luong and Quoc V. Le}, journal={2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2019 . 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. 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. Self-training with Noisy Student improves ImageNet classification. Med. 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 iterate this process by putting back the student as the teacher. 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. Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical on ImageNet ReaL 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. Self-training with noisy student improves imagenet classification. Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. Self-training On, International journal of molecular sciences. EfficientNet-L0 is wider and deeper than EfficientNet-B7 but uses a lower resolution, which gives it more parameters to fit a large number of unlabeled images with similar training speed. First, a teacher model is trained in a supervised fashion. 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. The architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. 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. Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. To achieve strong results on ImageNet, the student model also needs to be large, typically larger than common vision models, so that it can leverage a large number of unlabeled images. 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. This model investigates a new method for incorporating unlabeled data into a supervised learning pipeline. 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 If nothing happens, download Xcode and try again. We obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. [^reference-9] [^reference-10] A critical insight was to . Self-training with Noisy Student improves ImageNet classication Qizhe Xie 1, Minh-Thang Luong , Eduard Hovy2, Quoc V. Le1 1Google Research, Brain Team, 2Carnegie Mellon University fqizhex, thangluong, qvlg@google.com, hovy@cmu.edu Abstract We present Noisy Student Training, a semi-supervised learning approach that works well even when . [50] used knowledge distillation on unlabeled data to teach a small student model for speech recognition. This material is presented to ensure timely dissemination of scholarly and technical work. ; 2006)[book reviews], Semi-supervised deep learning with memory, Proceedings of the European Conference on Computer Vision (ECCV), Xception: deep learning with depthwise separable convolutions, K. Clark, M. Luong, C. D. Manning, and Q. V. Le, Semi-supervised sequence modeling with cross-view training, E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le, AutoAugment: learning augmentation strategies from data, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, E. D. Cubuk, B. Zoph, J. Shlens, and Q. V. Le, RandAugment: practical data augmentation with no separate search, Z. Dai, Z. Yang, F. Yang, W. W. Cohen, and R. R. Salakhutdinov, Good semi-supervised learning that requires a bad gan, T. Furlanello, Z. C. Lipton, M. Tschannen, L. Itti, and A. Anandkumar, A. Galloway, A. Golubeva, T. Tanay, M. Moussa, and G. W. Taylor, R. Geirhos, P. Rubisch, C. Michaelis, M. Bethge, F. A. Wichmann, and W. Brendel, ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness, J. Gilmer, L. Metz, F. Faghri, S. S. Schoenholz, M. Raghu, M. Wattenberg, and I. Goodfellow, I. J. Goodfellow, J. Shlens, and C. Szegedy, Explaining and harnessing adversarial examples, Semi-supervised learning by entropy minimization, Advances in neural information processing systems, K. Gu, B. Yang, J. Ngiam, Q. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. On . 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. 3.5B weakly labeled Instagram images. This way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled images. Agreement NNX16AC86A, Is ADS down? 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. We used the version from [47], which filtered the validation set of ImageNet. Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. The inputs to the algorithm are both labeled and unlabeled images. If nothing happens, download Xcode and try again. 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. 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. Infer labels on a much larger unlabeled dataset. Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. Papers With Code is a free resource with all data licensed under. During this process, we kept increasing the size of the student model to improve the performance. Self-Training With Noisy Student Improves ImageNet Classification. The top-1 accuracy is simply the average top-1 accuracy for all corruptions and all severity degrees. 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. This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. In the following, we will first describe experiment details to achieve our results. You signed in with another tab or window. 3429-3440. . 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. 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. 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. In Noisy Student, we combine these two steps into one because it simplifies the algorithm and leads to better performance in our preliminary experiments. Yalniz et al. 10687-10698 Abstract possible. This is a recurring payment that will happen monthly, If you exceed more than 500 images, they will be charged at a rate of $5 per 500 images. 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. Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. Code is available at this https URL.Authors: Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. LeLinks:YouTube: https://www.youtube.com/c/yannickilcherTwitter: https://twitter.com/ykilcherDiscord: https://discord.gg/4H8xxDFBitChute: https://www.bitchute.com/channel/yannic-kilcherMinds: https://www.minds.com/ykilcherParler: https://parler.com/profile/YannicKilcherLinkedIn: https://www.linkedin.com/in/yannic-kilcher-488534136/If you want to support me, the best thing to do is to share out the content :)If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this):SubscribeStar (preferred to Patreon): https://www.subscribestar.com/yannickilcherPatreon: https://www.patreon.com/yannickilcherBitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cqEthereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9mMonero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n IEEE Transactions on Pattern Analysis and Machine Intelligence. The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative Computer Science - Computer Vision and Pattern Recognition. Noisy Student Training seeks to improve on self-training and distillation in two ways. In all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. In this section, we study the importance of noise and the effect of several noise methods used in our model. We then train a larger EfficientNet as a student model on the Sun, Z. Liu, D. Sedra, and K. Q. Weinberger, Y. Huang, Y. Cheng, D. Chen, H. Lee, J. Ngiam, Q. V. Le, and Z. Chen, GPipe: efficient training of giant neural networks using pipeline parallelism, A. Iscen, G. Tolias, Y. Avrithis, and O. ImageNet-A top-1 accuracy from 16.6 Then we finetune the model with a larger resolution for 1.5 epochs on unaugmented labeled images. 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. Summarization_self-training_with_noisy_student_improves_imagenet_classification. 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. 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. As we use soft targets, our work is also related to methods in Knowledge Distillation[7, 3, 26, 16]. By clicking accept or continuing to use the site, you agree to the terms outlined in our. 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. 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. 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. 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. The pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. all 12, Image Classification Similar to[71], we fix the shallow layers during finetuning. The abundance of data on the internet is vast. Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled data. We then select images that have confidence of the label higher than 0.3. Flip probability is the probability that the model changes top-1 prediction for different perturbations. We iterate this process by putting back the student as the teacher. The hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. 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). First, we run an EfficientNet-B0 trained on ImageNet[69]. EfficientNet-L1 approximately doubles the training time of EfficientNet-L0. Models are available at this https URL. Train a larger classifier on the combined set, adding noise (noisy student). The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. 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. w Summary of key results compared to previous state-of-the-art models. Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. 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. Different types of. 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. In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. 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. We then use the teacher model to generate pseudo labels on unlabeled images. 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. Their noise model is video specific and not relevant for image classification. Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. For each class, we select at most 130K images that have the highest confidence. Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. 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. 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. A common workaround is to use entropy minimization or ramp up the consistency loss. Hence, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels. This paper standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications, and proposes a new dataset called ImageNet-P which enables researchers to benchmark a classifier's robustness to common perturbations. However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. 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. Imaging, 39 (11) (2020), 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. If nothing happens, download GitHub Desktop and try again. Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. We will then show our results on ImageNet and compare them with state-of-the-art models. Use a model to predict pseudo-labels on the filtered data: This is not an officially supported Google product. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. Do imagenet classifiers generalize to imagenet? Please refer to [24] for details about mFR and AlexNets flip probability. 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. These CVPR 2020 papers are the Open Access versions, provided by the. 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. Self-training with Noisy Student improves ImageNet classification. We start with the 130M unlabeled images and gradually reduce the number of images. Especially unlabeled images are plentiful and can be collected with ease. As shown in Table2, Noisy Student with EfficientNet-L2 achieves 87.4% top-1 accuracy which is significantly better than the best previously reported accuracy on EfficientNet of 85.0%. Further, Noisy Student outperforms the state-of-the-art accuracy of 86.4% by FixRes ResNeXt-101 WSL[44, 71] that requires 3.5 Billion Instagram images labeled with tags. On robustness test sets, it improves ImageNet-A top . As shown in Figure 1, Noisy Student leads to a consistent improvement of around 0.8% for all model sizes. Are labels required for improving adversarial robustness? 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. sign in We iterate this process by Image Classification 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. 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.