通常情况下,训练一个优秀的稠密检索模型离不开大规模的人工标注数据,然而,在很多应用场景和业务问题上,这种与领域相关的大规模标注数据非常难以获得,因此稠密检索模型的零样本域外泛化能力(zero-shot OOD generalizability)就变得非常重要。在实际应用中,不同领域之间通常存在较大差异,这种zero-shot能力直接影响着稠密检索...
通常情况下,训练一个优秀的稠密检索模型离不开大规模的人工标注数据,然而,在很多应用场景和业务问题上,这种与领域相关的大规模标注数据非常难以获得,因此稠密检索模型的零样本域外泛化能力(zero-shot OOD generalizability)就变得非常重要。在实际应用中,不同领域之间通常存在较大差异,这种zero-shot能力直接影响着稠密检索...
通常情况下,训练一个优秀的稠密检索模型离不开大规模的人工标注数据,然而,在很多应用场景和业务问题上,这种与领域相关的大规模标注数据非常难以获得,因此稠密检索模型的零样本域外泛化能力(zero-shot OOD generalizability)就变得非常重要。在实际应用中,不同领域之间通常存在较大差异,这种zero-shot能力直接影响着稠密检索...
那么，模型对于ood generalizability的需求也就显现出来了。联系与区别 随着语言模型的发展，zero-shot的概...
Zero-shot Sketch-based Image Retrieval with Adaptive Balanced Discriminability and Generalizability Zero-shot sketch-based image retrieval (ZS-SBIR) is a task that learns semantic knowledge and embedding extraction to retrieve similar images using a sketc... J Tian,X Xu,Z Cao,... - 《Proceedings...
We further investigated the generalizability of the model on an independent zero-shot test set of 440 aggregates that were significantly larger [Math Processing Error](100<Ns<1000) than the ones the model was trained on. An additional 440 large aggregates were used as a zero-shot validation ...
(Ns<100). We tested the model on an independent test set of 7656 aggregates with the same distribution of parameters as the training data set (Ns<100). We further investigated the generalizability of the model on an independent zero-shot test set of 440 aggregates that were significantly ...
Next, we evaluated CREaTor’s performance on autosomes of the K562 cell line (cross-cell type test chromosomes), which were unseen by the model, to demonstrate the generalizability of our method (Fig. 1b and Additional file 1: Fig. S2). For the in-cell type test chromosomes, CREaTor ...
Zero-shot Text-To-Speech (TTS) synthesis shows great promise for personalized voice customization through voice cloning. However, current methods for achieving zero-shot TTS heavily rely on large model scales and extensive training datasets to ensure satisfactory performance and generalizability across var...
RBGN employs the adversarial attack to train a more rigorous discriminator, thus enhancing the generalizability and robustness of the feature generator under minimax strategy. Moreover, RBGN decodes the generated visual features back to their semantic representations to further improve the representational...