self training with noisy student improves imagenet classification self training with noisy student improves imagenet classification

The width. Probably due to the same reason, at =16, EfficientNet-L2 achieves an accuracy of 1.1% under a stronger attack PGD with 10 iterations[43], which is far from the SOTA results. Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. We duplicate images in classes where there are not enough images. As shown in Figure 1, Noisy Student leads to a consistent improvement of around 0.8% for all model sizes. 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. 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. Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. 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]. Noisy Student can still improve the accuracy to 1.6%. Train a classifier on labeled data (teacher). 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. The pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). After testing our models robustness to common corruptions and perturbations, we also study its performance on adversarial perturbations. We apply dropout to the final classification layer with a dropout rate of 0.5. Papers With Code is a free resource with all data licensed under. It implements SemiSupervised Learning with Noise to create an Image Classification. We use the same architecture for the teacher and the student and do not perform iterative training. Summarization_self-training_with_noisy_student_improves_imagenet_classification. On . Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. IEEE Transactions on Pattern Analysis and Machine Intelligence. 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. We iterate this process by putting back the student as the teacher. Selected images from robustness benchmarks ImageNet-A, C and P. Test images from ImageNet-C underwent artificial transformations (also known as common corruptions) that cannot be found on the ImageNet training set. To noise the student, we use dropout[63], data augmentation[14] and stochastic depth[29] during its training. 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. 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. An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. 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. (or is it just me), Smithsonian Privacy Are you sure you want to create this branch? 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. Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. A number of studies, e.g. Soft pseudo labels lead to better performance for low confidence data. [2] show that Self-Training is superior to Pre-training with ImageNet Supervised Learning on a few Computer . 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. The most interesting image is shown on the right of the first row. A. Krizhevsky, I. Sutskever, and G. E. Hinton, Temporal ensembling for semi-supervised learning, Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks, Workshop on Challenges in Representation Learning, ICML, Certainty-driven consistency loss for semi-supervised learning, C. Liu, B. Zoph, M. Neumann, J. Shlens, W. Hua, L. Li, L. Fei-Fei, A. Yuille, J. Huang, and K. Murphy, R. G. Lopes, D. Yin, B. Poole, J. Gilmer, and E. D. Cubuk, Improving robustness without sacrificing accuracy with patch gaussian augmentation, Y. Luo, J. Zhu, M. Li, Y. Ren, and B. 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Pang, Robustness properties of facebooks resnext wsl models, Adversarial dropout for supervised and semi-supervised learning, Lessons from building acoustic models with a million hours of speech, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), S. Qiao, W. Shen, Z. Zhang, B. Wang, and A. Yuille, Deep co-training for semi-supervised image recognition, I. Radosavovic, P. Dollr, R. Girshick, G. Gkioxari, and K. He, Data distillation: towards omni-supervised learning, A. Rasmus, M. Berglund, M. Honkala, H. Valpola, and T. Raiko, Semi-supervised learning with ladder networks, E. Real, A. Aggarwal, Y. Huang, and Q. V. Le, Proceedings of the AAAI Conference on Artificial Intelligence, B. Recht, R. Roelofs, L. Schmidt, and V. Shankar. 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. (Submitted on 11 Nov 2019) We present a simple self-training method that achieves 87.4% top-1 accuracy on ImageNet, which is 1.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. We obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. 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 3.5B weakly labeled Instagram images. 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. and surprising gains on robustness and adversarial benchmarks. 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. Self-training with Noisy Student. EfficientNet with Noisy Student produces correct top-1 predictions (shown in. We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. 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. Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. Our work is based on self-training (e.g.,[59, 79, 56]). 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. Addressing the lack of robustness has become an important research direction in machine learning and computer vision in recent years. 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. The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical Especially unlabeled images are plentiful and can be collected with ease. 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). mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline. We use stochastic depth[29], dropout[63] and RandAugment[14]. Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. 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. 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. 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. For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. 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. team using this approach not only surpasses the top-1 ImageNet accuracy of SOTA models by 1%, it also shows that the robustness of a model also improves. 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. task. Add a unlabeled images. 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 evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. Notice, Smithsonian Terms of The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 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. ImageNet-A top-1 accuracy from 16.6 In addition to improving state-of-the-art results, we conduct additional experiments to verify if Noisy Student can benefit other EfficienetNet models. et al. 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. Use Git or checkout with SVN using the web URL. Le. 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. See 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. By clicking accept or continuing to use the site, you agree to the terms outlined in our. Callback to apply noisy student self-training (a semi-supervised learning approach) based on: Xie, Q., Luong, M. T., Hovy, E., & Le, Q. V. (2020). First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. We then train a larger EfficientNet as a student model on the In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. 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. ImageNet images and use it as a teacher to generate pseudo labels on 300M Astrophysical Observatory. mCE (mean corruption error) is the weighted average of error rate on different corruptions, with AlexNets error rate as a baseline. Self-Training Noisy Student " " Self-Training . to use Codespaces. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. The biggest gain is observed on ImageNet-A: our method achieves 3.5x higher accuracy on ImageNet-A, going from 16.6% of the previous state-of-the-art to 74.2% top-1 accuracy. If nothing happens, download GitHub Desktop and try again. [50] used knowledge distillation on unlabeled data to teach a small student model for speech recognition. , have shown that computer vision models lack robustness. One might argue that the improvements from using noise can be resulted from preventing overfitting the pseudo labels on the unlabeled images. Self-training to use Codespaces. In the following, we will first describe experiment details to achieve our results. The hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. 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. For each class, we select at most 130K images that have the highest confidence. Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. This invariance constraint reduces the degrees of freedom in the model. 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 hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. Are you sure you want to create this branch? In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. To achieve this result, we first train an EfficientNet model on labeled In Noisy Student, we combine these two steps into one because it simplifies the algorithm and leads to better performance in our preliminary experiments. Use Git or checkout with SVN using the web URL. Hence the total number of images that we use for training a student model is 130M (with some duplicated images). 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. 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. 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. We then perform data filtering and balancing on this corpus. 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, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels.