![]() ’86, founding chairman of the China Advisory Board and executive chairman of 111 Inc., a health company in China, formed a team with members of his company and was able to immediately procure thousands of N95 masks directly from 111’s Shanghai warehouse. It’s a testament to their enduring commitment to Cornell that so many alumni, along with parents and friends, both at home and abroad, answered the call.” “The shipments of personal protective equipment and other supplies are allowing our dedicated health care team to safely and effectively provide the best care to our COVID patients. “We are incredibly grateful to Cornell’s China Advisory Board for mobilizing the community to help during this time of great need,” Choi said. Choi, the Stephen and Suzanne Weiss Dean of Weill Cornell Medicine and provost for medical affairs of Cornell University, reached out to the university’s China Advisory Board for assistance, the response was immediate. ![]() Members of the alumni community began mobilizing support and supplies as soon as COVID-19 appeared in New York, and when Wendy Wolford, vice provost for international affairs, and Dr. The resolution recognized members of the Cornell China Advisory Board, along with Cornell’s Chinese alumni and parent community members, for their “extraordinary work and generosity” and the important role their collective efforts have played in “assisting Weill Cornell Medicine during a global pandemic … supporting Cornell University and protecting medical professionals and vulnerable members of the local population.” On April 10, the Executive Committee of the Cornell Board of Trustees passed a resolution of gratitude for these efforts, specifically calling out the thousands of surgical and N95 masks collected and sent to New York City medical professionals treating COVID-19 patients. Barry Zhu, chief operations officer for 111 Inc., is pictured at the Shanghai airport. Whether built upon ResNet or ViT, we achieve the new state of the art for CD-FSL. Extensive experiments conducted on eight various target datasets show the effectiveness of our method. Besides the typical CNN-based backbone, we also employ our StyleAdv method on large-scale pretrained vision transformer. By continually attacking styles and forcing the model to recognize these challenging adversarial styles, our model is gradually robust to the visual styles, thus boosting the generalization ability for novel target datasets. This is achieved by perturbing the original style with the signed style gradients. Particularly, our style attack method synthesizes both "virtual" and "hard" adversarial styles for model training. ![]() Thus, inspired by vanilla adversarial learning, a novel model-agnostic meta Style Adversarial training (StyleAdv) method together with a novel style adversarial attack method is proposed for CD-FSL. Such a vanilla operation makes the generated styles "real" and "easy", which still fall into the original set of the source styles. However, wave-SAN simply swaps styles of two images. Critically, such a domain gap actually comes from the changes of visual styles, and wave-SAN empirically shows that spanning the style distribution of the source data helps alleviate this issue. The CD-FSL task is especially challenged by the huge domain gap between different datasets. It aims at transferring prior knowledge learned on the source dataset to novel target datasets. Cross-Domain Few-Shot Learning (CD-FSL) is a recently emerging task that tackles few-shot learning across different domains.
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