EfficientNet: rethinking model scaling for convolutional neural networks. In Proceedings of the 36th International Conference on Machine Learning (ICML 2019) 6105–6114 (PMLR, 2019). Hu, J., Shen, L. & Sun, G. Squeeze-and-excitation networks. In 2018 IEEE/CVF Conference on Computer Vision ...
Given an image I, and a natural language prompt P, PromptCap generates a prompt-guided caption C. P contains instructions about the image contents of interest to the user. For VQA, an example prompt could be “Please describe this image according to the following question: what type plane ...
Z. Scaling provable adversarial defenses. In Advances in Neural Information Processing Systems 8400–8409 (NIPS, 2018). Su, D. et al. Is robustness the cost of accuracy? A comprehensive study on the robustness of 18 deep image classification models. In European Conference on Computer Vision 631...
That is, the distortion and classification error has the following relation: distortion = a + b · log (classification-error). The fitted parameters of a and b are given in the captions of Figure 2. Take I-FGSM attack as an example, the linear scaling law suggests that to reduce the ...
parameters ofaandbare given in the captions of Fig.2. Take I-FGSM attack as an example, the linear scaling law suggests that to reduce the classification error by a half, the\ell _\inftydistortion of the resulting network will be expected to reduce by approximately 0.02, which is roughly...
(1). 3.4. Multi-Stage Multi-Scale Framework We further adopt a multi-stage framework because we find it more effective, as compared to scaling up the model width or height (see ablation Sec. 4.3A). We deem full resolution processing [14, 63, 70] a better approach than a multi-patch ...
We insert an identity matrix I (Chw,Chw) and use the associative law V(out)=V(in)⋅(I⋅W(F,p)⊺). (11) We note that because W(F,p) is constructed with F, I⋅W(F,p)⊺ is a convolution with F on a feature map M(I) which is reshaped from I....
tcpluess/tikz-imagelabelsPublic Notifications Fork0 Star3 Code Issues1 Pull requests Actions Projects Security Insights Additional navigation options Files master .gitignore Makefile README.md pleiades.jpg tikz-imagelabels.dtx tikz-imagelabels.ins ...
Artificial intelligence models match or exceed dermatologists in melanoma image classification. Less is known about their robustness against real-world variations, and clinicians may incorrectly assume that a model with an acceptable area under the recei
Automated medical image analysis systems often require large amounts of training data with high quality labels, which are difficult and time consuming to generate. This paper introduces Radiology Object in COntext version 2 (ROCOv2), a multimodal dataset