Zero-Shot Task Transfer

By Arghya Pal, Vineeth Nallure Balasubramanian. 2019

54 citations recorded by the source corpus.

In this work, we present a novel meta-learning algorithm that regresses model parameters for novel tasks for which no ground truth is available (zero-shot tasks). In order to adapt to novel zero-shot tasks, our meta-learner learns from the model parameters of known tasks (with ground truth) and the correlation of known tasks to zero-shot tasks. Such intuition finds its foothold in cognitive science, where a subject (human baby) can adapt to a novel concept (depth understanding) by correlating it with old concepts (hand movement or self-motion), without receiving an explicit supervision. We evaluated our model on the Taskonomy dataset, with four tasks as zero-shot: surface normal, room layout, depth and camera pose estimation. These tasks were chosen based on the data acquisition complexity and the complexity associated with the learning process using a deep network. Our proposed methodolgy outperforms state-of-the-art models (which use ground truth) on each of our zero-shot tasks, showing promise on zero-shot task transfer. We also conducted extensive experiments to study the various choices of our methodology, as well as showed how the proposed method can also be used in transfer learning. To the best of our knowledge, this is the first such effort on zero-shot learning in the task space.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Meta-Consolidation for Continual Learning (2020, background): “This opened up an interesting research direction, in exploiting the meta-manifold of model parameters for multi-task learning [66], neural architecture search [8], zero-shot learning [57, 81] and style transfer [72] in recent years.”
Bowtie Networks: Generative Modeling for Joint Few-Shot Recognition and Novel-View Synthesis (2020, methodology): “[20] proposed a metalearning algorithm to adapt existing models to novel zero-shot learning tasks.”
A Meta-Learning Framework for Generalized Zero-Shot Learning (2019, methodology): “Because of the wide applicability and more realistic setting the ZSL framework also applied on the different domain like Zero-Shot Task Transfer [34], zero-shot sketch-based image retrieval [42, 23], zero-shot knowledge distillation [31], zero-shot action recognition [56, 13, 30, 28] etc.”
Transforming task representations to perform novel tasks (2020, methodology): “50, 87.34]).”