In this way, MRA can not only conduct strong nonlinear augmentation to train robust deep neural networks but also regulate the generation with similar high-level semantics bounded by the reconstruction task. Image inpaintingBertalmio et al. tackle complex vision tasks but expose undesirable properties like the. Official implementation of the paper Masked Autoencoders are Robust Data Augmentors. By combining CutMix, MRAachieve 78.93% top-1 accuracy on ImageNet, which outperforms carefully designed mixed strategyUddin et al. To this end, regularization techniques like image . To this end, regularization techniques like image augmentation are necessary for deep neural networks to generalize well. Deep neural networks are capable of learning powerful representations to tackle complex vision tasks but expose undesirable properties like the over-fitting issue. We also compare the results of pretraining with CutMix augmentation. It is consistent with our intuition that the attention-based masking can be seen as an advanced Cutout. To this end, regularization techniques like image augmentation are necessary for deep neural networks to generalize well. The synthesized image data works well in low-data regimeAntoniou et al. In this paper, we show that video masked autoencoders (VideoMAE) are data-efficient learners for self-supervised video pre-training (SSVP). Learn more. (2012); Simonyan and Zisserman (2014); He et al. Due to their hand-crafted property, these augmentations are insufficient to generate truly hard augmented examples. By tuning the masking ratio, we show that a much smaller MAE-Mini can achieve better performance with 6 speed up and 95% parameter decrease compared to the MAE-Large. (2018) where collecting datasets is inconvenient, like the medical imagingYi et al. task. In this paper, we closely follow the model architecture of MAEHe et al. Masked Autoencoders are Robust Data Augmentors. We evaluate few-shot classificationChen et al. Masked . Masked Autoencoders are Robust Data Augmentors. # 1. Abstract:As a promising scheme of self-supervised learning, masked autoencoding has significantly advanced natural language processing and computer vision. ( 2019), Cutout DeVries and Taylor ( 2017) and Mixup Zhang et al. No 47. In this section, we conduct several ablation studies to dissect the effect of each component. When masking a high-attention area, the model degrades the classification performance by over 1% compared to baseline. Masked Autoencoders are Robust Data Augmentors &MAECutMixCutout Mixup!! Deep neural networks are capable of learning powerful representations to tackle complex vision tasks but expose undesirable properties like the over-fitting issue. (2014) and variational auto-encodersKingma and Welling (2013), we can generate new training data that help the model obtain smoother decision boundaries. Test-time training adapts to a new test distribution on the fly by optimizing a model for each test input using self-supervision. Due to their hand-crafted property, these augmentations are insufficient to generate truly hard augmented examples. (2018), manifesting mask autoencoders are robust data augmentors. When only generating the masked regions, the augmentation can be controllable but strong because of the non-linearity. These manual-designed methods are fast, reproducible and reliable to encode the invariance of color and geometric space on the original dataset. If nothing happens, download Xcode and try again. FasterAAHataya et al. (2019) adopts a more efficient policy via density matching. We maintain the patches with high attention as input and erase the rest of the patches. We also compare the GPU hours of pretraining and pre-searching on ImageNet, MRAalso has an affordable computation cost compared with AutoAugment and Fast AutoAugment. Deep neural networks are capable of learning powerful representations to tackle complex vision tasks but expose undesirable properties like the over-fitting issue. According to the analysis inChen et al. In machine learning, we can see the applications of autoencoder at various places, largely in unsupervised learning. Nevertheless, most prevalent image . As shown in Table 2, MRAconsistently improves the performance on fine-grained classification. To this end, regularization techniques like image augmentation are necessary for deep neural networks to generalize well. Official implementation of the paper Masked Autoencoders are Robust Data Augmentors. MAEmask . - "Masked Autoencoders are Robust Data Augmentors" . A tag already exists with the provided branch name. (2018), automated data augmentation methods have made remarkable progress over the past few years. The base categories and novel categories are not overlapped. It is not surprising since the larger model captures more accurate attention information and provides stronger regularization. Instead of using conventional image manipulation, a line of works introduce generative adversarial networks (GAN)Goodfellow et al. We term the proposed method as Mask-Reconstruct Augmentation (MRA). share 0 research 3 months ago DE-Net: Dynamic Text-guided Image Editing Adversarial Networks Text-guided image editing models have shown remarkable results. Moreover, though both random masking and low-attention masking raise the accuracy, low-attention dropping rules is superior with a further nearly 0.7% gain. Are you sure you want to create this branch? Then the resized 224224 images are fed into the pretrained MRAmodule to perform the mask-and-reconstruct operation. Recent workChen et al. Besides, there is another line of work utilizing inter-samples to train the model more robustly. Search 204,908,239 papers from all fields of science. We show that utilizing such model-based nonlinear transformation as data augmentation can improve high-level recognition tasks. reveal that these low-level transformations can be easily grasped by the deep neural network, which demonstrates that such basic image processing methods may be insuffificient to effectively generalize the input distribution. " life """ . (2015); Redmon et al. Year. The extensive experiments on various image classification benchmarks verify the effectiveness of the proposed augmentation. MAEmask . (2016), object detectionRen et al. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. We're hiring! Then, we divide the masked image Mx into non-overlapped patches and discard the masked patches. (2019), the model is trained for 300 epochs using an SGD optimizer with a momentum of 0.9 and weight decay of 0.00006. Inspired by this, we propose a neat scheme of masked autoencoders for point cloud self-supervised learning, addressing the challenges posed by point Inspired by the recent success of applying masked image modeling to self-supervised learning, we adopt the self-supervised masked autoencoder to generate the distorted view of the input images. (2018). To alleviate the overfitting issue, data augmentationsLeCun et al. Masked Autoencoders are Robust Data Augmentors. We report the corresponding classification accuracy in Table6. Masked Autoencoders are Robust Data Augmentors. In this section, we evaluate MRAon several classification tasks, including fully supervised, semi-supervised, and few-show classification. In this paper, we propose a novel perspective of augmentation to regularize the training process. . FixMatchSohn et al. In light of the success of neural architecture search (NAS)Cai et al. We evaluate MRA on multiple image classification benchmarks. This paper proposes a novel hybrid framework termed Siamese Transition Masked . (2021). As shown in Table 1, MRAachieves 78.35% top-1 accuracy using ResNet-50 as backbone, which outperforms a series of automated augmentations searching methods. augmentation are necessary for deep neural networks to generalize well. The code will be available at https://github.com/haohang96/MRA. MixupZhang et al. We show that utilizing such model-based nonlinear transformation as data augmentation can improve high-level recognition tasks. Pretraining Epochs is an important hyper-parameter for self-supervised learning. (2021); Liu et al. 2020. (2021); Bao et al. Hence, we need to make the generation more controllable. Deep neural networks are capable of learning powerful representations to tackle complex vision tasks but expose undesirable properties like the over-fitting issue. "Masked Autoencoders are Robust Data Augmentors"arxiv_zbw-ITS203. However, an extremely small masking ratio will also make the pretraining task too easy, which may influence the generalization ability of the pretrained MAE-Mini. In few-shot learning, a large number of labeled training samples are given on some base categories first, then the goal is to predict on novel categories where only a few K-shot samples are labeled. But such sample synthesis method can not generalize well to a large-scale labeled datasetDeng et al. We ablate the model size of MAE. In detail, we pretrain an extremely light-weight autoencoder via a self-supervised mask-reconstruct strategyHe et al. Deep neural networks are capable of learning powerful representations to tackle complex vision tasks but expose undesirable properties like the over-fitting issue. (2013), randomly cropping, flipping, and color jittering in ImageNet and CIFAR classificationKrizhevsky et al. ( 2018), manifesting mask autoencoders are robust data augmentors. Cited by. Specifically, MRA consistently enhances the performance on supervised, semi-supervised as well as few-shot classification. Motivated by image inpainting, our method, termed as Mask-Reconstruct Augmentation (MRA), targets at recovering part of the images instead of adversarial learning. If nothing happens, download GitHub Desktop and try again. Click To Get Model/Code. has renewed a surge of interest due to its capacity to learn useful representations from rich unlabeled data. , GANGAN, model-basedimage inpaintingMask-Reconstruct Augmentation MRAself-supervised mask-reconstruct strategyMAEmask, MRA, MAEMAEMRA, MSE, TransformerViTtokentoken, token:k, , MRA. Table 8: ImageNet classification accuracy with/without reconstruction. 2However, recent works on self-supervised learning [21. ] [LG]Forecasting Future World Events with Neural NetworksA. No 46.100. Most inpainting methods follow the pipeline of a context encoderPathak et al. Enhancing Self-supervised Video Representation Learning via Multi-level Feature Optimization. These methods modify the masking process of MADE, according to conditional dependencies inferred from the MRF structure, to reduce either the model complexity or the problem . Meanwhile, we train an decoder D with parameters to recover original image from masked images latent embeddings: ^x=D(E(Mx)), where ^x designates the reconstructed image. , Then, we use simple isotonic regression and histogram statistics to estimate P(w ij | eij) and P(eij | wij), https://blog.csdn.net/qq_43497436/article/details/126054212, PICO: CONTRASTIVE LABEL DISAMBIGUATION FOR PARTIAL LABEL LEARNINGICLR2022, CCGL: Contrastive Cascade Graph LearningTKDE2022, Momentum contrast for unsupervised visual representation learningCVPR2020, Augmentation-Free Self-Supervised Learning on Graphs(AAAI 2022), Deep Graph Clustering via Dual Correlation ReductionAAAI2022, Parallelly Adaptive Graph Convolutional Clustering Model(TNNLS2022), MPC: Multi-View Probabilistic Clustering(CVPR2022), Embedding Graph Auto-Encoder for Graph Clustering(TNNLS2022), Semi-supervised classification with graph convolutional networks(ICLR2017). As a result, the generated objects might be in any ridiculous shape and appearance, differing greatly from what they were previously distributed. At the same time, they enjoy the label-preserving property that the transformations conducted over an image would not change the high-level semantic information. To this end, controllable image reconstruction is a good choice for generating a similar likelihood distribution. (2019), we further evaluate the robustness of MRAby generating boundary occluded validation samples. The reconstructed images can be directly exploited to compute classification loss. (2020) can achieve the better results by introducing inter-sample regularization. In this section, we introduce our Mask-Reconstruct Augmentation (MRA). No 45.AI. All results in semi-supervised classification are trained with the code-base in, The pretrained MAE-Base and MAE-Large models are directly downloaded from, Data augmentation generative adversarial networks, M. Assran, M. Caron, I. Misra, P. Bojanowski, F. Bordes, P. Vincent, A. Joulin, M. Rabbat, and N. Ballas, Masked siamese networks for label-efficient learning, Beit: bert pre-training of image transformers, M. Bertalmio, G. Sapiro, V. Caselles, and C. Ballester, Proceedings of the 27th annual conference on Computer graphics and interactive techniques, D. Berthelot, N. Carlini, I. Goodfellow, N. Papernot, A. Oliver, and C. A. Raffel, Mixmatch: a holistic approach to semi-supervised learning, Advances in Neural Information Processing Systems, Proxylessnas: direct neural architecture search on target task and hardware, M. Caron, H. Touvron, I. Misra, H. Jgou, J. Mairal, P. Bojanowski, and A. Joulin, Emerging properties in self-supervised vision transformers, Proceedings of the IEEE/CVF International Conference on Computer Vision, L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs, IEEE transactions on pattern analysis and machine intelligence, W. Chen, Y. Liu, Z. Kira, Y. F. Wang, and J. Huang, International Conference on Learning Representations, X. Chen, M. Ding, X. Wang, Y. Xin, S. Mo, Y. Wang, S. Han, P. Luo, G. Zeng, and J. Wang, Context autoencoder for self-supervised representation learning, Multi-column deep neural networks for image classification, 2012 IEEE conference on computer vision and pattern recognition, An analysis of single-layer networks in unsupervised feature learning, Proceedings of the fourteenth international conference on artificial intelligence and statistics, E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le, Autoaugment: learning augmentation strategies from data, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, E. D. Cubuk, B. Zoph, J. Shlens, and Q. V. Le, Randaugment: practical automated data augmentation with a reduced search space, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, J. Deng, W. Dong, R. Socher, L. Li, K. Li, and L. Fei-Fei, Imagenet: a large-scale hierarchical image database, 2009 IEEE conference on computer vision and pattern recognition, Improved regularization of convolutional neural networks with cutout. Denoising Autoencoders dates back to 2012, was introduced as a way to make AEs more robust, mainly as a criterion on the loss function. Denoising Masked AutoEncoders (DMAE), for learning certified robust classifiers of images. However,. Note that CutMixYun et al. (2019) proposes to search for optimal combination of each augmentation magnitude. There are works found that augmentations generated by DCGANRadford et al. dont have to squint at a PDF. No 44. (2021). (2021) has shown that vision transformers trained with no supervision can automatically learn object-related representation. (2018); Madani et al. Unsupervised visual anomaly detection conveys practical significance in many scenarios and is a challenging task due to the unbounded definition of anomalies. Inspired by the recent success of applying masked image modeling to self-supervised learning, we adopt the self-supervised masked autoencoder to generate the distorted view of the input images. (2021, 2018), but suffers poor performance on the test set. . In practice, we find significantly squashing the model size of autoencoder remain a considerably high performance, which is reported in Table9. (2018) on miniImageNet dataset. Specifically, MRA consistently enhances the performance on supervised, semi-supervised as well as few-shot classification. 1. (2020). This work does not have a direct negative social impact. Introduction. (2019) for a fair comparison. Inspired by the recent success of applying masked image modeling to self-supervised learning, we adopt the self-supervised masked autoencoder to generate the distorted view of the input images. This kind of augmentation is unsuitable for dense prediction tasks such as instance segmentation since generative augmentation can easily destroy the boundary of the instance. MAE&MAECutMixCutoutMixup 2022.1ADAPTIVE IMAGE INPAINTING, 2022.1ADAPTIVE IMAGE INPAINTING Moreover, most previous methods are application-specific, and establishing a unified model for anomalies across application scenarios remains unsolved. Usually, these models are then trained on 10% of the data with labels to perform downstream tasks such as object detection and semantic segmentation. (2018); Zhang et al. (2018), we conjecture the CutMix augmentation leads to a severe over-fitting on base categories, which induces the failure of transferring to novel classes. IsAmant: Cannot retrieve contributors at this time. Nevertheless, most prevalent image augmentation recipes confine themselves to off-the-shelf linear transformations like scale, flip, and colorjitter. Through the revolution of backbone models, training datasets, optimization methods. Nevertheless, most prevalent image augmentation recipes confine themselves to off-the-shelf linear transformations like scale, flip, and colorjitter. We show that erasing the label-unrelated noisy patches leads to a more expected and constrained generation, which is highly beneficial to the stable training and enhances the object awareness of the model. As shown in Figure4, hole size of 0 means that the whole image is occluded, while hole size of 224 denotes that the input image is not occluded. Closely following the recent self-supervised method MAEHe et al. Masking is a process of hiding information of the data from the models. MRA boosts the performance uniformly among a bunch of classification benchmarks, demonstrating the effectiveness and robustness of MRA. (2019) copies a random patch from one image and pastes it into another image, which significantly boosts the robustness and performance. Therefore, the uncertain and unstable properties of GAN limit its application in image augmentation. Given the pretrained encoder E, we can compute attention maps for each input patch. We keep the hyper-parameters exactly the same during running the baseline supervised experiments and our MRAexperiments to make sure the comparison is fair. We further constrain the generation by introducing an attention-based masking strategy, which denoises the training and distills object-aware representations. Besides, thanks to generative adversarial networksGoodfellow et al. MRAachieves the lowest error among three augmentations which demonstrates that our mask-and-reconstruct pipeline generates occlusion-robust augmentation. A. Efros, Context encoders: feature learning by inpainting, Unsupervised representation learning with deep convolutional generative adversarial networks, A. Rasmus, M. Berglund, M. Honkala, H. Valpola, and T. Raiko, Semi-supervised learning with ladder networks, J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, You only look once: unified, real-time object detection, Faster r-cnn: towards real-time object detection with region proposal networks, E. Schwartz, L. Karlinsky, J. Shtok, S. Harary, M. Marder, A. Kumar, R. Feris, R. Giryes, and A. Bronstein, Delta-encoder: an effective sample synthesis method for few-shot object recognition, A survey on image data augmentation for deep learning, Very deep convolutional networks for large-scale image recognition, Prototypical networks for few-shot learning, K. Sohn, D. Berthelot, N. Carlini, Z. Zhang, H. Zhang, C. A. Raffel, E. D. Cubuk, A. Kurakin, and C. Li, Fixmatch: simplifying semi-supervised learning with consistency and confidence, F. Sung, Y. Yang, L. Zhang, T. Xiang, P. H. Torr, and T. M. Hospedales, Learning to compare: relation network for few-shot learning, Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results, H. Touvron, M. Cord, M. Douze, F. Massa, A. Sablayrolles, and H. Jgou, Training data-efficient image transformers & distillation through attention, A. S. Uddin, M. S. Monira, W. Shin, T. Chung, and S. Bae, SaliencyMix: a saliency guided data augmentation strategy for better regularization. The ultimate architecture of MRA is displayed in Figure1. The direction of generative augmentations remains unexplored on mainstream image recognition benchmarks. (2021). Use masking to make autoencoders understand the visual world. A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, An image is worth 16x16 words: transformers for image recognition at scale, Y. Fang, L. 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Isola, J. Zhu, T. Zhou, and A. To this end, regularization techniques like image augmentation are necessary for deep neural networks to generalize well. At the same time, they enjoy the label-preserving property that the transformations conducted over an image would not change the high-level semantic information. (2016), which infers the missing parts with a generator network using pixel-wise reconstruction loss, and a discriminator to distinguish whether the recovered image is real or fake. A. Efros, Image-to-image translation with conditional adversarial networks, B. Kang, S. Xie, M. Rohrbach, Z. Yan, A. Gordo, J. Feng, and Y. Kalantidis, Decoupling representation and classifier for long-tailed recognition, Puzzle mix: exploiting saliency and local statistics for optimal mixup, International Conference on Machine Learning, J. Krause, M. Stark, J. Deng, and L. Fei-Fei, 3d object representations for fine-grained categorization, Proceedings of the IEEE international conference on computer vision workshops, A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks, Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, Gradient-based learning applied to document recognition, Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks, Workshop on challenges in representation learning, ICML, Y. Li, G. Hu, Y. Wang, T. Hospedales, N. M. Robertson, and Y. Yang, DADA: differentiable automatic data augmentation, S. Lim, I. Kim, T. Kim, C. Kim, and S. Kim, Z. Liu, H. Mao, C. Wu, C. Feichtenhofer, T. Darrell, and S. Xie, Fully convolutional networks for semantic segmentation, A. Madani, M. Moradi, A. Karargyris, and T. Syeda-Mahmood, Chest x-ray generation and data augmentation for cardiovascular abnormality classification, S. Maji, E. Rahtu, J. Kannala, M. Blaschko, and A. Vedaldi, Fine-grained visual classification of aircraft, Data augmentation for improving deep learning in image classification problem, 2018 international interdisciplinary PhD workshop (IIPhDW), T. Miyato, S. Maeda, M. Koyama, and S. Ishii, Virtual adversarial training: a regularization method for supervised and semi-supervised learning, D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. This logical dropping of connections is done with the help of masks and hence the name Masked Autoencoder. Unless specified, we conduct all ablation studies on the ImageNet dataset for 90 epochs using ResNet50 as backbone, and report the Top-1 accuracy of the validation set.
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