Task Embedding
Task Embedding is a representational encoding that captures the distinctive characteristics of a learning task within a shared latent space, enabling meta-learning systems to generalize across tasks by modeling task-specific inductive biases as compact vectorial signatures.
30
Publications
2.1K
Citations
120
Authors
61
Institutions
Publications per year
2017–2026
24
Parents
30
Yuning Lu, Jianzhuang Liu, Yonggang Zhang et al. · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · 2022 · 217 citations
TAFE-Net: Task-Aware Feature Embeddings for Low Shot Learning
Xin Wang, Fisher Yu, Ruth Wang et al. · 2019 · 113 citations
1–5 of 30
120
2
h-index
2
h-index
2
h-index
2
h-index
2
h-index
1–5 of 120
61
Huawei Technologies (Sweden)
Stockholm, Sweden
3 h-index
6 publications
342 citations
Tsinghua University
Beijing, China
2 h-index
2 publications
56 citations

Northwestern Polytechnical University
Xi'an, China
2 h-index
6 publications
426 citations

University of Massachusetts Amherst
Amherst Center, United States
2 h-index
2 publications
68 citations
University of California, Irvine
Irvine, United States
2 h-index
2 publications
76 citations
Stanford University
Stanford, United States
2 h-index
3 publications
707 citations
California Institute of Technology
Pasadena, United States
1 h-index
1 publications
35 citations
Shanghai Institute of Applied Physics
Pudong, China
1 h-index
1 publications
20 citations
1–8 of 61
Repository · 5 publications · 119 citations
5
h-index
Conference · 3 publications · 60 citations
3
h-index
Repository · 1 publications · 21 citations
1
h-index
1 publications · 20 citations
1
h-index
1 publications · 22 citations
1
h-index
1–5