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Shot in the Dark: Few-Shot Learning with No Base-Class Labels

Shot in the Dark: Few-Shot Learning with No Base-Class Labels

6 October 2020
Z. Chen
Subhransu Maji
Erik Learned-Miller
    SSL
    VLM
ArXivPDFHTML

Papers citing "Shot in the Dark: Few-Shot Learning with No Base-Class Labels"

5 / 5 papers shown
Title
A Survey of the Self Supervised Learning Mechanisms for Vision Transformers
A Survey of the Self Supervised Learning Mechanisms for Vision Transformers
Asifullah Khan
A. Sohail
M. Fiaz
Mehdi Hassan
Tariq Habib Afridi
...
Muhammad Zaigham Zaheer
Kamran Ali
Tangina Sultana
Ziaurrehman Tanoli
Naeem Akhter
45
3
0
30 Aug 2024
Distributed Machine Learning for Wireless Communication Networks:
  Techniques, Architectures, and Applications
Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applications
Shuyan Hu
Xiaojing Chen
Wei Ni
E. Hossain
Xin Wang
AI4CE
42
111
0
02 Dec 2020
Improved Baselines with Momentum Contrastive Learning
Improved Baselines with Momentum Contrastive Learning
Xinlei Chen
Haoqi Fan
Ross B. Girshick
Kaiming He
SSL
267
3,369
0
09 Mar 2020
Delta-encoder: an effective sample synthesis method for few-shot object
  recognition
Delta-encoder: an effective sample synthesis method for few-shot object recognition
Eli Schwartz
Leonid Karlinsky
J. Shtok
Sivan Harary
Mattias Marder
Rogerio Feris
Abhishek Kumar
Raja Giryes
A. Bronstein
186
351
0
12 Jun 2018
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Chelsea Finn
Pieter Abbeel
Sergey Levine
OOD
317
11,681
0
09 Mar 2017
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