Imagenet
Found 5 free book(s)
Bootstrap Your Own Latent A New Approach to Self ...
arxiv.orgIn the semi-supervised and transfer settings on ImageNet, we obtain results on par or superior to the current state of the art. Our contributions are: (i) We introduce BYOL, a self-supervised representation learning method (Section3) which achieves state-of-the-art results under the linear evaluation protocol on ImageNet without using negative ...
Going Deeper With Convolutions - Computer Science
www.cs.unc.eduous fine-grained visual categories such as those in ImageNet (even in the 1000-class ILSVRC subset) as shown in Fig-ure 1. The other drawback of uniformly increased network size is the dramatically increased use of computational re-sources. For example, in a deep vision network, if two convolutional layers are chained, any uniform increase in
Software Engineering for Machine Learning: A Case Study
www.microsoft.comgeneric datasets (e.g., ImageNet for object detection), and then use transfer learning together with more specialized data to. train a more specific model (e.g., pedestrian detection). Data cleaning involves removing inaccurate or noisy records from
Lecture 13: Generative Models
cs231n.stanford.edu32x32 CIFAR-10 32x32 ImageNet. Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 13 - 33 May 18, 2017 PixelRNN and PixelCNN Improving PixelCNN performance - Gated convolutional layers - Short-cut connections - Discretized logistic loss - Multi-scale - Training tricks
![arXiv:2110.00476v1 [cs.CV] 1 Oct 2021](/cache/preview/2/7/e/8/b/0/d/d/thumb-27e8b0dd4b2ecdef99deca68d379ca38.jpg)
arXiv:2110.00476v1 [cs.CV] 1 Oct 2021
arxiv.orgthe literature, the performance reported on ImageNet-1k-val for this architecture ranges from 75.2% to 79.5%, depending on the paper. It is unclear whether a sufficient effort has been invested in pushing the baseline further. We want to fill this gap: in this pa-per, we focus on the vanilla ResNet-50 architecture2 as described by He et al ...