self training with noisy student improves imagenet classification

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. However, manually annotating organs from CT scans is time . To achieve this result, we first train an EfficientNet model on labeled Are you sure you want to create this branch? We vary the model size from EfficientNet-B0 to EfficientNet-B7[69] and use the same model as both the teacher and the student. In addition to improving state-of-the-art results, we conduct additional experiments to verify if Noisy Student can benefit other EfficienetNet models. Use a model to predict pseudo-labels on the filtered data: This is not an officially supported Google product. It is expensive and must be done with great care. 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. In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. Notably, EfficientNet-B7 achieves an accuracy of 86.8%, which is 1.8% better than the supervised model. The baseline model achieves an accuracy of 83.2. student is forced to learn harder from the pseudo labels. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. Finally, for classes that have less than 130K images, we duplicate some images at random so that each class can have 130K images. Learn more. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. In this section, we study the importance of noise and the effect of several noise methods used in our model. We present a simple self-training method that achieves 87.4 As shown in Figure 1, Noisy Student leads to a consistent improvement of around 0.8% for all model sizes. Med. As we use soft targets, our work is also related to methods in Knowledge Distillation[7, 3, 26, 16]. If nothing happens, download GitHub Desktop and try again. The top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. Our work is based on self-training (e.g.,[59, 79, 56]). There was a problem preparing your codespace, please try again. Self-Training Noisy Student " " Self-Training . Next, a larger student model is trained on the combination of all data and achieves better performance than the teacher by itself.OUTLINE:0:00 - Intro \u0026 Overview1:05 - Semi-Supervised \u0026 Transfer Learning5:45 - Self-Training \u0026 Knowledge Distillation10:00 - Noisy Student Algorithm Overview20:20 - Noise Methods22:30 - Dataset Balancing25:20 - Results30:15 - Perturbation Robustness34:35 - Ablation Studies39:30 - Conclusion \u0026 CommentsPaper: https://arxiv.org/abs/1911.04252Code: https://github.com/google-research/noisystudentModels: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnetAbstract:We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. The performance consistently drops with noise function removed. It can be seen that masks are useful in improving classification performance. We use the same architecture for the teacher and the student and do not perform iterative training. This invariance constraint reduces the degrees of freedom in the model. For instance, on ImageNet-A, Noisy Student achieves 74.2% top-1 accuracy which is approximately 57% more accurate than the previous state-of-the-art model. (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. In the following, we will first describe experiment details to achieve our results. Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. Noisy Student (B7) means to use EfficientNet-B7 for both the student and the teacher. To noise the student, we use dropout[63], data augmentation[14] and stochastic depth[29] during its training. A self-training method that better adapt to the popular two stage training pattern for multi-label text classification under a semi-supervised scenario by continuously finetuning the semantic space toward increasing high-confidence predictions, intending to further promote the performance on target tasks. Noisy Student Training seeks to improve on self-training and distillation in two ways. 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. Self-Training with Noisy Student Improves ImageNet Classification It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images 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. possible. Then, that teacher is used to label the unlabeled data. Lastly, we apply the recently proposed technique to fix train-test resolution discrepancy[71] for EfficientNet-L0, L1 and L2. 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. 3429-3440. . Self-training with Noisy Student improves ImageNet classification The architectures for the student and teacher models can be the same or different. 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. About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . Notice, Smithsonian Terms of 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. A tag already exists with the provided branch name. Hence the total number of images that we use for training a student model is 130M (with some duplicated images). Are labels required for improving adversarial robustness? 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 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. 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. to use Codespaces. (using extra training data). 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). sign in on ImageNet ReaL Infer labels on a much larger unlabeled dataset. [57] used self-training for domain adaptation. We do not tune these hyperparameters extensively since our method is highly robust to them. Self-training with Noisy Student improves ImageNet classification. 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]. 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. In this work, we showed that it is possible to use unlabeled images to significantly advance both accuracy and robustness of state-of-the-art ImageNet models. Classification of Socio-Political Event Data, SLADE: A Self-Training Framework For Distance Metric Learning, Self-Training with Differentiable Teacher, https://github.com/hendrycks/natural-adv-examples/blob/master/eval.py. 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]. The main use case of knowledge distillation is model compression by making the student model smaller. The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. 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. If nothing happens, download GitHub Desktop and try again. An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. Code for Noisy Student Training. The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). Yalniz et al. 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. Different kinds of noise, however, may have different effects. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. 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. 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]. Self-Training With Noisy Student Improves ImageNet Classification. A tag already exists with the provided branch name. In particular, we first perform normal training with a smaller resolution for 350 epochs. We determine number of training steps and the learning rate schedule by the batch size for labeled images. Please refer to [24] for details about mCE and AlexNets error rate. Our procedure went as follows. For simplicity, we experiment with using 1128,164,132,116,14 of the whole data by uniformly sampling images from the the unlabeled set though taking the images with highest confidence leads to better results. Noisy Student Explained | Papers With Code It implements SemiSupervised Learning with Noise to create an Image Classification. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. 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. 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. We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. Scripts used for our ImageNet experiments: Similar scripts to run predictions on unlabeled data, filter and balance data and train using the filtered data. 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). Self-training with noisy student improves imagenet classification. Self-training is a form of semi-supervised learning [10] which attempts to leverage unlabeled data to improve classification performance in the limited data regime. 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. 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. On ImageNet-C, it reduces mean corruption error (mCE) from 45.7 to 31.2. As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. Self-Training With Noisy Student Improves ImageNet Classification Semi-supervised medical image classification with relation-driven self-ensembling model. 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 . Their framework is highly optimized for videos, e.g., prediction on which frame to use in a video, which is not as general as our work. 27.8 to 16.1. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. For smaller models, we set the batch size of unlabeled images to be the same as the batch size of labeled images. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. 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. 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. 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. Amongst other components, Noisy Student implements Self-Training in the context of Semi-Supervised Learning. The results also confirm that vision models can benefit from Noisy Student even without iterative training. unlabeled images. 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. 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. Self-training with Noisy Student improves ImageNet classification. 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. Zoph et al. 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. This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. supervised model from 97.9% accuracy to 98.6% accuracy. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. Self-training with Noisy Student improves ImageNet classification As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. Ranked #14 on 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. Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. 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. We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. In our experiments, we observe that soft pseudo labels are usually more stable and lead to faster convergence, especially when the teacher model has low accuracy. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior.

Houses For Rent In Lycoming County, Pa, Mychart Methodist Dallas, Hobart Football Coach Under Investigation, Millennium Buck Hut Replacement Cover, Keyshia Sweat Mother, Articles S