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

The performance consistently drops with noise function removed. 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. 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. EfficientNet-L1 approximately doubles the training time of EfficientNet-L0. First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. 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. Learn more. 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. For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. While removing noise leads to a much lower training loss for labeled images, we observe that, for unlabeled images, removing noise leads to a smaller drop in training loss. We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. 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. Self-training was previously used to improve ResNet-50 from 76.4% to 81.2% top-1 accuracy[76] which is still far from the state-of-the-art accuracy. 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 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. This way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled images. We also study the effects of using different amounts of unlabeled data. 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. The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. Similar to[71], we fix the shallow layers during finetuning. Edit social preview. In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. The accuracy is improved by about 10% in most settings. mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline. After testing our models robustness to common corruptions and perturbations, we also study its performance on adversarial perturbations. Self-training with Noisy Student improves ImageNet classification 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. 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]. [^reference-9] [^reference-10] A critical insight was to . Scripts used for our ImageNet experiments: Similar scripts to run predictions on unlabeled data, filter and balance data and train using the filtered data. combination of labeled and pseudo labeled images. Please Amongst other components, Noisy Student implements Self-Training in the context of Semi-Supervised Learning. 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]. A semi-supervised segmentation network based on noisy student learning 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. Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. Use Git or checkout with SVN using the web URL. The model with Noisy Student can successfully predict the correct labels of these highly difficult images. Our work is based on self-training (e.g.,[59, 79, 56]). The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . Test images on ImageNet-P underwent different scales of perturbations. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Why Self-training with Noisy Students beats SOTA Image classification Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores. A tag already exists with the provided branch name. 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. Flip probability is the probability that the model changes top-1 prediction for different perturbations. Code for Noisy Student Training. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. The baseline model achieves an accuracy of 83.2. 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. 27.8 to 16.1. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. We sample 1.3M images in confidence intervals. The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. 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. Self-training with Noisy Student improves ImageNet classification To date (2020) we will introduce "Noisy Student Training", which is a state-of-the-art model.The idea is to extend self-training and Distillation, a paper that shows that by adding three noises and distilling multiple times, the student model will have better generalization performance than the teacher model. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Noisy Student improves adversarial robustness against an FGSM attack though the model is not optimized for adversarial robustness. On, International journal of molecular sciences. Especially unlabeled images are plentiful and can be collected with ease. Self-Training With Noisy Student Improves ImageNet Classification This material is presented to ensure timely dissemination of scholarly and technical work. 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. In all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. The main use case of knowledge distillation is model compression by making the student model smaller. 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. 3429-3440. . In other words, the student is forced to mimic a more powerful ensemble model. The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). Efficient Nets with Noisy Student Training | by Bharatdhyani | Towards 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. 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. In terms of methodology, First, a teacher model is trained in a supervised fashion. 2023.3.1_2 - to noise the student. 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. For smaller models, we set the batch size of unlabeled images to be the same as the batch size of labeled images. For instance, on ImageNet-1k, Layer Grafted Pre-training yields 65.5% Top-1 accuracy in terms of 1% few-shot learning with ViT-B/16, which improves MIM and CL baselines by 14.4% and 2.1% with no bells and whistles. The architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. 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. Chowdhury et al. We iterate this process by and surprising gains on robustness and adversarial benchmarks. Self-training with Noisy Student improves ImageNet classification Abstract. For RandAugment, we apply two random operations with the magnitude set to 27. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 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 top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. 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. We use the standard augmentation instead of RandAugment in this experiment. Their noise model is video specific and not relevant for image classification. The algorithm is basically self-training, a method in semi-supervised learning (. If nothing happens, download Xcode and try again. 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. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (teacher). 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, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. ImageNet-A test set[25] consists of difficult images that cause significant drops in accuracy to state-of-the-art models. Astrophysical Observatory. unlabeled images. We investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. Self-training with Noisy Student improves ImageNet classification Self-training with Noisy Student improves ImageNet classification Notably, EfficientNet-B7 achieves an accuracy of 86.8%, which is 1.8% better than the supervised model. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled 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. 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 architectures for the student and teacher models can be the same or different. Self-Training With Noisy Student Improves ImageNet Classification Our main results are shown in Table1. We determine number of training steps and the learning rate schedule by the batch size for labeled images. 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 study shows that using unlabeled data improves accuracy and general robustness. 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. They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. 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. Code is available at this https URL.Authors: Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. 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