self training with noisy student improves imagenet classification

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. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. At the top-left image, the model without Noisy Student ignores the sea lions and mistakenly recognizes a buoy as a lighthouse, while the model with Noisy Student can recognize the sea lions. Self-training with Noisy Student improves ImageNet classification Abstract. Do better imagenet models transfer better? The top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. 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. 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. Do imagenet classifiers generalize to imagenet? We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. Our main results are shown in Table1. This way, the pseudo labels are as good as possible, and the noised student is forced to learn harder from the pseudo labels. (2) With out-of-domain unlabeled images, hard pseudo labels can hurt the performance while soft pseudo labels leads to robust performance. We use EfficientNet-B0 as both the teacher model and the student model and compare using Noisy Student with soft pseudo labels and hard pseudo labels. [68, 24, 55, 22]. 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. This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. This is a recurring payment that will happen monthly, If you exceed more than 500 images, they will be charged at a rate of $5 per 500 images. 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. self-mentoring outperforms data augmentation and self training. Hence the total number of images that we use for training a student model is 130M (with some duplicated images). We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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. Self-Training With Noisy Student Improves ImageNet Classification 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.

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