If nothing happens, download GitHub Desktop and try again. Noisy Student Explained | Papers With Code The width. The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). Finally, in the above, we say that the pseudo labels can be soft or hard. A tag already exists with the provided branch name. 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 duplicate images in classes where there are not enough images. Computer Science - Computer Vision and Pattern Recognition. 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. unlabeled images. Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. This work adopts the noisy-student learning method, and adopts 3D nnUNet as the segmentation model during the experiments, since No new U-Net is the state-of-the-art medical image segmentation method and designs task-specific pipelines for different tasks. With Noisy Student, the model correctly predicts dragonfly for the image. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. task. We then perform data filtering and balancing on this corpus. In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. 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. 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. We do not tune these hyperparameters extensively since our method is highly robust to them. 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. Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. 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. labels, the teacher is not noised so that the pseudo labels are as good as 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. During this process, we kept increasing the size of the student model to improve the performance. The main use case of knowledge distillation is model compression by making the student model smaller. Please refer to [24] for details about mCE and AlexNets error rate. We conduct experiments on ImageNet 2012 ILSVRC challenge prediction task since it has been considered one of the most heavily benchmarked datasets in computer vision and that improvements on ImageNet transfer to other datasets. In Noisy Student, we combine these two steps into one because it simplifies the algorithm and leads to better performance in our preliminary experiments. possible. 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. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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]. We present a simple self-training method that achieves 87.4 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. ImageNet-A test set[25] consists of difficult images that cause significant drops in accuracy to state-of-the-art models. In all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. First, we run an EfficientNet-B0 trained on ImageNet[69]. [57] used self-training for domain adaptation. For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. Due to duplications, there are only 81M unique images among these 130M images. 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 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. There was a problem preparing your codespace, please try again. 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. 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. 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. Self-training with Noisy Student - 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. We also list EfficientNet-B7 as a reference. https://arxiv.org/abs/1911.04252. . We use the standard augmentation instead of RandAugment in this experiment. (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. For classes where we have too many images, we take the images with the highest confidence. SelfSelf-training with Noisy Student improves ImageNet classification Self-Training With Noisy Student Improves ImageNet Classification. 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. Unlike previous studies in semi-supervised learning that use in-domain unlabeled data (e.g, ., CIFAR-10 images as unlabeled data for a small CIFAR-10 training set), to improve ImageNet, we must use out-of-domain unlabeled data. Noisy Student can still improve the accuracy to 1.6%. The top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. 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). In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. Imaging, 39 (11) (2020), pp. Code for Noisy Student Training. We find that using a batch size of 512, 1024, and 2048 leads to the same performance. 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. However, manually annotating organs from CT scans is time . 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. We start with the 130M unlabeled images and gradually reduce the number of images. But during the learning of the student, we inject noise such as data ImageNet . The architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. 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. This material is presented to ensure timely dissemination of scholarly and technical work. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (teacher). We iterate this process by putting back the student as the teacher. Self-training with Noisy Student improves ImageNet classification 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 This paper presents a unique study of transfer learning with large convolutional networks trained to predict hashtags on billions of social media images and shows improvements on several image classification and object detection tasks, and reports the highest ImageNet-1k single-crop, top-1 accuracy to date. We iterate this process by 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. See (2) With out-of-domain unlabeled images, hard pseudo labels can hurt the performance while soft pseudo labels leads to robust performance. 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. 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. Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. 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. . By clicking accept or continuing to use the site, you agree to the terms outlined in our. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. 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. On robustness test sets, it improves Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a Code is available at https://github.com/google-research/noisystudent. During the generation of the pseudo 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. We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. Then we finetune the model with a larger resolution for 1.5 epochs on unaugmented labeled images. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. 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. 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. Train a classifier on labeled data (teacher). In addition to improving state-of-the-art results, we conduct additional experiments to verify if Noisy Student can benefit other EfficienetNet models. The top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. 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. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. The method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. mCE (mean corruption error) is the weighted average of error rate on different corruptions, with AlexNets error rate as a baseline. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Scripts used for our ImageNet experiments: Similar scripts to run predictions on unlabeled data, filter and balance data and train using the filtered data. Self-Training Noisy Student " " Self-Training . 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. ImageNet images and use it as a teacher to generate pseudo labels on 300M 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. 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 . Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. The performance drops when we further reduce it. Le. 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. 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]. 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. In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. Noisy Student self-training is an effective way to leverage unlabelled datasets and improving accuracy by adding noise to the student model while training so it learns beyond the teacher's knowledge. Self-training with Noisy Student improves ImageNet classification The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. This invariance constraint reduces the degrees of freedom in the model. Self-training with Noisy Student improves ImageNet classification 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. 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 . Stochastic depth is proposed, a training procedure that enables the seemingly contradictory setup to train short networks and use deep networks at test time and reduces training time substantially and improves the test error significantly on almost all data sets that were used for evaluation. Their main goal is to find a small and fast model for deployment. Noisy Student Training seeks to improve on self-training and distillation in two ways. Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled data. Self-training with Noisy Student improves ImageNet classification Self-mentoring: : A new deep learning pipeline to train a self Infer labels on a much larger unlabeled dataset. Noisy Student improves adversarial robustness against an FGSM attack though the model is not optimized for adversarial robustness. Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. Z. Yalniz, H. Jegou, K. 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