self training with noisy student improves imagenet classification

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. They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. labels, the teacher is not noised so that the pseudo labels are as good as You signed in with another tab or window. 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. 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]. 2023.3.1_2 - We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Soft pseudo labels lead to better performance for low confidence data. On . 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. putting back the student as the teacher. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. Learn more. 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. Using self-training with Noisy Student, together with 300M unlabeled images, we improve EfficientNets[69] ImageNet top-1 accuracy to 87.4%. We start with the 130M unlabeled images and gradually reduce the number of images. Use Git or checkout with SVN using the web URL. self-mentoring outperforms data augmentation and self training. See Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. The most interesting image is shown on the right of the first row. 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. On ImageNet-C, it reduces mean corruption error (mCE) from 45.7 to 31.2. Semi-supervised medical image classification with relation-driven self-ensembling model. We use EfficientNet-B4 as both the teacher and the student. Diagnostics | Free Full-Text | A Collaborative Learning Model for Skin 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. 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. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. We apply dropout to the final classification layer with a dropout rate of 0.5. The algorithm is basically self-training, a method in semi-supervised learning (. Papers With Code is a free resource with all data licensed under. After using the masks generated by teacher-SN, the classification performance improved by 0.2 of AC, 1.2 of SP, and 0.7 of AUC. Hence the total number of images that we use for training a student model is 130M (with some duplicated images). There was a problem preparing your codespace, please try again. Hence, a question that naturally arises is why the student can outperform the teacher with soft 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. 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. Add a This model investigates a new method for incorporating unlabeled data into a supervised learning pipeline. We iterate this process by putting back the student as the teacher. [^reference-9] [^reference-10] A critical insight was to . This work introduces two challenging datasets that reliably cause machine learning model performance to substantially degrade and curates an adversarial out-of-distribution detection dataset called IMAGENET-O, which is the first out- of-dist distribution detection dataset created for ImageNet models. Noisy StudentImageNetEfficientNet-L2state-of-the-art. Models are available at this https URL. On ImageNet-P, it leads to an mean flip rate (mFR) of 17.8 if we use a resolution of 224x224 (direct comparison) and 16.1 if we use a resolution of 299x299.111For EfficientNet-L2, we use the model without finetuning with a larger test time resolution, since a larger resolution results in a discrepancy with the resolution of data and leads to degraded performance on ImageNet-C and ImageNet-P. Please refer to [24] for details about mFR and AlexNets flip probability. Figure 1(b) shows images from ImageNet-C and the corresponding predictions. The top-1 accuracy is simply the average top-1 accuracy for all corruptions and all severity degrees. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. FixMatch-LS: Semi-supervised skin lesion classification with label 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. The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). Here we use unlabeled images to improve the state-of-the-art ImageNet accuracy and show that the accuracy gain has an outsized impact on 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. 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 will then show our results on ImageNet and compare them with state-of-the-art models. 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. Distillation Survey : Noisy Student | 9to5Tutorial 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. Train a classifier on labeled data (teacher). However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. Parthasarathi et al. This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. Train a larger classifier on the combined set, adding noise (noisy student). If nothing happens, download GitHub Desktop and try again. Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores. unlabeled images. PDF Self-Training with Noisy Student Improves ImageNet Classification 3429-3440. . Then, that teacher is used to label the unlabeled data. Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. Here we study how to effectively use out-of-domain data. We improved it by adding noise to the student to learn beyond the teachers knowledge. Noisy Student (B7, L2) means to use EfficientNet-B7 as the student and use our best model with 87.4% accuracy as the teacher model. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. By clicking accept or continuing to use the site, you agree to the terms outlined in our. corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from 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. The top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. 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. Noisy Student (EfficientNet) - huggingface.co Zoph et al. mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline. Self-Training With Noisy Student Improves ImageNet Classification Are you sure you want to create this branch? 27.8 to 16.1. Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. 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. and surprising gains on robustness and adversarial benchmarks. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. Different kinds of noise, however, may have different effects. 10687-10698 Abstract Chowdhury et al. Finally, for classes that have less than 130K images, we duplicate some images at random so that each class can have 130K images. We then train a larger EfficientNet as a student model on the If nothing happens, download Xcode and try again. We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. The ONCE (One millioN sCenEs) dataset for 3D object detection in the autonomous driving scenario is introduced and a benchmark is provided in which a variety of self-supervised and semi- supervised methods on the ONCE dataset are evaluated. Self-Training : Noisy Student : Similar to[71], we fix the shallow layers during finetuning. Noisy Student Explained | Papers With Code 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. An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. Use, Smithsonian Iterative training is not used here for simplicity. In the following, we will first describe experiment details to achieve our results. Their noise model is video specific and not relevant for image classification. During the generation of the pseudo

self training with noisy student improves imagenet classification 2023