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Our experiments show the limitation of learning universal representations from both classification and self-supervised tasks, demonstrated by the performance differences in self-training and pre-training
Rethinking Pre-training and Self-training
NIPS 2020, (2020)
Pre-training is a dominant paradigm in computer vision. For example, supervised ImageNet pre-training is commonly used to initialize the backbones of object detection and segmentation models. He et al., however, show a surprising result that ImageNet pre-training has limited impact on COCO object detection. Here we investigate self-trai...More
- Pre-training is a dominant paradigm in computer vision. As many vision tasks are related, it is expected a model, pre-trained on one dataset, to help another.
- It is common practice to pre-train the backbones of object detection and segmentation models on ImageNet classification [2,3,4,5].
- This practice has been recently challenged by He et al , who show a surprising result that such ImageNet pre-training does not improve accuracy on the COCO dataset.
- Can self-training work well on the exact setup, using ImageNet to improve COCO, where pre-training fails?
- Pre-training is a dominant paradigm in computer vision
- We study ImageNet models pre-trained using a state-of-the-art self-supervised learning technique and compare to standard supervised ImageNet pre-training on COCO
- Our work argues for the scalability and generality of self-training (e.g., [6,7,8])
- Our experiments show the limitation of learning universal representations from both classification and self-supervised tasks, demonstrated by the performance differences in self-training and pre-training
- Our intuition for the weak performance of pre-training is that pre-training is not aware of the task of interest and can fail to adapt. Such adaptation is often needed when switching tasks because, for example, good features for ImageNet may discard positional information which is needed for COCO
- We argue that jointly training the self-training objective with supervised learning is more adaptive to the task of interest
- Data Augmentation: The authors use four different augmentation policies of increasing strength that work for both detection and segmentation.
- The authors design the augmentation policies based on the standard flip and crop augmentation in the literature , AutoAugment [48, 49], and RandAugment .
- The standard flip and crop policy consists of horizontal flips and scale jittering .
- AutoAugment and RandAugment are originally designed with the standard scale jittering.
- The last three augmentation policies are stronger than He et al  who use only a FlipCrop-based strategy
- All of the baselines are stronger than He et al  who only use ResNets for their experimentation (EfficientNetB7 checkpoint has an approximately 8% higher accuracy than a ResNet-50 checkpoint).
- Table 6 shows that the method improves state-of-the-art by a large margin.
- The authors achieve 90.5% mIOU on the PASCAL VOC 2012 test set using single-scale inference, outperforming the old state-of-the-art 89% mIOU which utilizes multi-scale inference.
- For PASCAL, the authors find pre-training with a good checkpoint to be crucial, without it the authors achieve 41.5 % mIOU.
- The authors' model improves the previous state-of-the-art by 1.5% mIOU even using much less human labels in training
- The authors' experiments show the limitation of learning universal representations from both classification and self-supervised tasks, demonstrated by the performance differences in self-training and pre-training.
- The authors' intuition for the weak performance of pre-training is that pre-training is not aware of the task of interest and can fail to adapt.
- Such adaptation is often needed when switching tasks because, for example, good features for ImageNet may discard positional information which is needed for COCO.
- The authors suspect that this leads self-training to be more generally beneficial
- Table1: Notations for data augmentations and pre-trained models used throughout this work
- Table2: In regimes where pre-training hurts, self-training with the same data source helps. All models are trained on the full COCO dataset
- Table3: Self-training improves performance for all model initializations across all labeled dataset sizes. All models are trained on COCO using Augment-S4
- Table4: Self-supervised pre-training (SimCLR) hurts performance on COCO just like standard supervised pre-training. Performance of ResNet-50 backbone model with different model initializations on full COCO. All models use Augment-S4
- Table5: Comparison with the strong models on COCO object detection. Self-training results use the
- Table6: Comparison with state-of-the-art models on PASCAL VOC 2012 val/test set. † indicates multi-scale/flip ensembling inference. ‡ indicates fine tuning the model on the train+val with hard classes being duplicated [<a class="ref-link" id="c18" href="#r18">18</a>]. EfficientNet models (Eff) are trained on PASCAL train set for validation results and train+val for test results. Self-training uses the aug set of PASCAL
- Table7: Comparison of pre-training, self-training and joint-training on COCO. All three methods use
- Table8: Performance on PASCAL VOC 2012 using train or train and aug for the labeled data
- Table9: Optimal α as a function of augmentation strength and training iterations. For each augmentation and training iteration settings the following α were tried: 0.25, 0.5, 1.0, 2.0, 3.0, 4.0
- Table10: Supervised semantic segmentation performance on PASCAL with different ImageNet pre-trained checkpoint qualities and data augmentation strengths
- Table11: Performance of our four different strength augmentation policies. The supervised model is a ResNet-101 with image size 640 × 640 using the standard training protocol from [<a class="ref-link" id="c14" href="#r14">14</a>]. ImageNet is used as the self-training dataset source
- Table12: Performance on different self-training dataset sources with varying augmentation strengths
- Table13: Performance on different source datasets for PASCAL Segmentation. All models are initialized using EfficientNet-B7 ImageNet++ checkpoint
- Pre-training has received much attention throughout the history of deep learning (see  and references therein). The resurgence of deep learning in the 2000s also began with unsupervised pre-training [20,21,22,23,24]. The success of unsupervised pre-training in NLP [25,26,27,28,29,30] has revived much interest in unsupervised pre-training in computer vision, especially contrastive training [13, 31,32,33,34,35]. In practice, supervised pre-training is highly successful in computer vision. For example, many studies, e.g., [36,37,38,39,40], have shown that ConvNets pre-trained on ImageNet, Instagram, and JFT can provide strong improvements for many downstream tasks.
Supervised ImageNet pre-training is the most widely-used initialization method for object detection and segmentation (e.g., [2,3,4,5]). He et al , however, demonstrate that ImageNet pre-training does not work well if we consider a much different task such as COCO object detection.
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