The depth of representations is of central importance for many visual recognition tasks. We also present analysis on CIFAR-10 with 1 layers. This result won the 1st place on the ILSVRC 2015 classification task. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set.
On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers-8× deeper than VGG nets but still having lower complexity. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We release all refined training data, training codes, pre-trained models and training logs, which will help reproduce the results in this paper.Ībstract: Deeper neural networks are more difficult to train. We show that ArcFace consistently outperforms the state-of-the-art and can be easily implemented with negligible computational overhead. We present arguably the most extensive experimental evaluation of all the recent state-of-the-art face recognition methods on over 10 face recognition benchmarks including a new large-scale image database with trillion level of pairs and a large-scale video dataset. The proposed ArcFace has a clear geometric interpretation due to the exact correspondence to the geodesic distance on the hypersphere. In this paper, we propose an Additive Angular Margin Loss (ArcFace) to obtain highly discriminative features for face recognition. Recently, a popular line of research is to incorporate margins in well-established loss functions in order to maximise face class separability. SphereFace assumes that the linear transformation matrix in the last fully connected layer can be used as a representation of the class centres in an angular space and penalises the angles between the deep features and their corresponding weights in a multiplicative way. Centre loss penalises the distance between the deep features and their corresponding class centres in the Euclidean space to achieve intra-class compactness.
Visualization of the attention layers shows that the generator leverages neighborhoods that correspond to object shapes rather than local regions of fixed shape.Ībstract: One of the main challenges in feature learning using Deep Convolutional Neural Networks (DCNNs) for large-scale face recognition is the design of appropriate loss functions that enhance discriminative power. The proposed SAGAN achieves the state-of-the-art results, boosting the best published Inception score from 36.8 to 52.52 and reducing Frechet Inception distance from 27.62 to 18.65 on the challenging ImageNet dataset. Leveraging this insight, we apply spectral normalization to the GAN generator and find that this improves training dynamics. Furthermore, recent work has shown that generator conditioning affects GAN performance. Moreover, the discriminator can check that highly detailed features in distant portions of the image are consistent with each other. In SAGAN, details can be generated using cues from all feature locations. Traditional convolutional GANs generate high-resolution details as a function of only spatially local points in lower-resolution feature maps.
So you should choose a PNG image of the right size.Abstract: In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks. Small PNG images can be distorted when converted to a large icon format (128x128, 256x256). PNG images of different sizes will lose some image information when converted to 16x16 size images. If your icon is used in the Favorites icon (favicon.ico) on the website, you only need to select 16x16 size. This tool does not support 16, 24 bit color or bitmap format with palette. This tool supports converting PNG images of any size into icons of various sizes, but only supports icons converted to 32-bit colors. These images are all in one file and its extension is ICO. For example, the title bar uses a 16x16 size image, the toolbar uses a 32x32 size image, and the desktop uses a 48x48 size image.
The Windows system reads images of different sizes depending on the usage scenario. It contains a list of images with various sizes of images, such as 16x16 and 32x32. The ICO format is an icon format for Windows systems. PNG images are a popular image format that includes a transparent background.