2022-07-07 更新
Difference in Euclidean Norm Can Cause Semantic Divergence in Batch Normalization
Authors:Zhennan Wang, Kehan Li, Runyi Yu, Yian Zhao, Pengchong Qiao, Guoli Song, Fan Xu, Jie Chen
In this paper, we show that the difference in Euclidean norm of samples can make a contribution to the semantic divergence and even confusion, after the spatial translation and scaling transformation in batch normalization. To address this issue, we propose an intuitive but effective method to equalize the Euclidean norms of sample vectors. Concretely, we $l_2$-normalize each sample vector before batch normalization, and therefore the sample vectors are of the same magnitude. Since the proposed method combines the $l_2$ normalization and batch normalization, we name our method as $L_2$BN. The $L_2$BN can strengthen the compactness of intra-class features and enlarge the discrepancy of inter-class features. In addition, it can help the gradient converge to a stable scale. The $L_2$BN is easy to implement and can exert its effect without any additional parameters and hyper-parameters. Therefore, it can be used as a basic normalization method for neural networks. We evaluate the effectiveness of $L_2$BN through extensive experiments with various models on image classification and acoustic scene classification tasks. The experimental results demonstrate that the $L_2$BN is able to boost the generalization ability of various neural network models and achieve considerable performance improvements.
PDF
点此查看论文截图
StyleFlow For Content-Fixed Image to Image Translation
Authors:Weichen Fan, Jinghuan Chen, Jiabin Ma, Jun Hou, Shuai Yi
Image-to-image (I2I) translation is a challenging topic in computer vision. We divide this problem into three tasks: strongly constrained translation, normally constrained translation, and weakly constrained translation. The constraint here indicates the extent to which the content or semantic information in the original image is preserved. Although previous approaches have achieved good performance in weakly constrained tasks, they failed to fully preserve the content in both strongly and normally constrained tasks, including photo-realism synthesis, style transfer, and colorization, etc. To achieve content-preserving transfer in strongly constrained and normally constrained tasks, we propose StyleFlow, a new I2I translation model that consists of normalizing flows and a novel Style-Aware Normalization (SAN) module. With the invertible network structure, StyleFlow first projects input images into deep feature space in the forward pass, while the backward pass utilizes the SAN module to perform content-fixed feature transformation and then projects back to image space. Our model supports both image-guided translation and multi-modal synthesis. We evaluate our model in several I2I translation benchmarks, and the results show that the proposed model has advantages over previous methods in both strongly constrained and normally constrained tasks.
PDF
点此查看论文截图
Generalization to translation shifts: a study in architectures and augmentations
Authors:Suriya Gunasekar
We provide a detailed evaluation of various image classification architectures (convolutional, vision transformer, and fully connected MLP networks) and data augmentation techniques towards generalization to large spacial translation shifts. We make the following observations: (a) In the absence of data augmentation, all architectures, including convolutional networks suffer degradation in performance when evaluated on translated test distributions. Understandably, both the in-distribution accuracy as well as degradation to shifts is significantly worse for non-convolutional architectures. (b) Across all architectures, even a minimal augmentation of $4$ pixel random crop improves the robustness of performance to much larger magnitude shifts of up to $1/4$ of image size ($8$-$16$ pixels) in the test data — suggesting a form of meta generalization from augmentation. For non-convolutional architectures, while the absolute accuracy is still low, we see dramatic improvements in robustness to large translation shifts. (c) With sufficiently advanced augmentation ($4$ pixel crop+RandAugmentation+Erasing+MixUp) pipeline all architectures can be trained to have competitive performance, both in terms of in-distribution accuracy as well as generalization to large translation shifts.
PDF
点此查看论文截图
How Much More Data Do I Need? Estimating Requirements for Downstream Tasks
Authors:Rafid Mahmood, James Lucas, David Acuna, Daiqing Li, Jonah Philion, Jose M. Alvarez, Zhiding Yu, Sanja Fidler, Marc T. Law
Given a small training data set and a learning algorithm, how much more data is necessary to reach a target validation or test performance? This question is of critical importance in applications such as autonomous driving or medical imaging where collecting data is expensive and time-consuming. Overestimating or underestimating data requirements incurs substantial costs that could be avoided with an adequate budget. Prior work on neural scaling laws suggest that the power-law function can fit the validation performance curve and extrapolate it to larger data set sizes. We find that this does not immediately translate to the more difficult downstream task of estimating the required data set size to meet a target performance. In this work, we consider a broad class of computer vision tasks and systematically investigate a family of functions that generalize the power-law function to allow for better estimation of data requirements. Finally, we show that incorporating a tuned correction factor and collecting over multiple rounds significantly improves the performance of the data estimators. Using our guidelines, practitioners can accurately estimate data requirements of machine learning systems to gain savings in both development time and data acquisition costs.
PDF Accepted to CVPR 2022
点此查看论文截图
2022-07-07 更新
DCT-Net: Domain-Calibrated Translation for Portrait Stylization
Authors:Yifang Men, Yuan Yao, Miaomiao Cui, Zhouhui Lian, Xuansong Xie
This paper introduces DCT-Net, a novel image translation architecture for few-shot portrait stylization. Given limited style exemplars ($\sim$100), the new architecture can produce high-quality style transfer results with advanced ability to synthesize high-fidelity contents and strong generality to handle complicated scenes (e.g., occlusions and accessories). Moreover, it enables full-body image translation via one elegant evaluation network trained by partial observations (i.e., stylized heads). Few-shot learning based style transfer is challenging since the learned model can easily become overfitted in the target domain, due to the biased distribution formed by only a few training examples. This paper aims to handle the challenge by adopting the key idea of “calibration first, translation later” and exploring the augmented global structure with locally-focused translation. Specifically, the proposed DCT-Net consists of three modules: a content adapter borrowing the powerful prior from source photos to calibrate the content distribution of target samples; a geometry expansion module using affine transformations to release spatially semantic constraints; and a texture translation module leveraging samples produced by the calibrated distribution to learn a fine-grained conversion. Experimental results demonstrate the proposed method’s superiority over the state of the art in head stylization and its effectiveness on full image translation with adaptive deformations.
PDF Accepted by SIGGRAPH 2022 (TOG). Project Page: https://menyifang.github.io/projects/DCTNet/DCTNet.html , Code: https://github.com/menyifang/DCT-Net
点此查看论文截图
DenseHybrid: Hybrid Anomaly Detection for Dense Open-set Recognition
Authors:Matej Grcić, Petra Bevandić, Siniša Šegvić
Anomaly detection can be conceived either through generative modelling of regular training data or by discriminating with respect to negative training data. These two approaches exhibit different failure modes. Consequently, hybrid algorithms present an attractive research goal. Unfortunately, dense anomaly detection requires translational equivariance and very large input resolutions. These requirements disqualify all previous hybrid approaches to the best of our knowledge. We therefore design a novel hybrid algorithm based on reinterpreting discriminative logits as a logarithm of the unnormalized joint distribution $\hat{p}(\mathbf{x}, \mathbf{y})$. Our model builds on a shared convolutional representation from which we recover three dense predictions: i) the closed-set class posterior $P(\mathbf{y}|\mathbf{x})$, ii) the dataset posterior $P(d_{in}|\mathbf{x})$, iii) unnormalized data likelihood $\hat{p}(\mathbf{x})$. The latter two predictions are trained both on the standard training data and on a generic negative dataset. We blend these two predictions into a hybrid anomaly score which allows dense open-set recognition on large natural images. We carefully design a custom loss for the data likelihood in order to avoid backpropagation through the untractable normalizing constant $Z(\theta)$. Experiments evaluate our contributions on standard dense anomaly detection benchmarks as well as in terms of open-mIoU - a novel metric for dense open-set performance. Our submissions achieve state-of-the-art performance despite neglectable computational overhead over the standard semantic segmentation baseline.
PDF Accepted on ECCV 2022
点此查看论文截图