Zero-shot Generalization
167 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Zero-shot Generalization
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CALVIN
Most implemented papers
Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation
prs-eth/marigold
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Monocular depth estimation is a fundamental computer vision task.
Zero-Shot Relation Extraction via Reading Comprehension
We show that relation extraction can be reduced to answering simple reading comprehension questions, by associating one or more natural-language questions with each relation slot.
Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics
The recent adaptation of deep neural network-based methods to reinforcement learning and planning domains has yielded remarkable progress on individual tasks.
Consistency by Agreement in Zero-shot Neural Machine Translation
google-research/language
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NAACL 2019
Generalization and reliability of multilingual translation often highly depend on the amount of available parallel data for each language pair of interest.
Schema-Guided Dialogue State Tracking Task at DSTC8
google-research-datasets/dstc8-schema-guided-dialogue
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The goal of this task is to develop dialogue state tracking models suitable for large-scale virtual assistants, with a focus on data-efficient joint modeling across domains and zero-shot generalization to new APIs.
Generalization to New Actions in Reinforcement Learning
clvrai/new-actions-rl
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ICML 2020
A fundamental trait of intelligence is the ability to achieve goals in the face of novel circumstances, such as making decisions from new action choices.
What Can I Do Here? Learning New Skills by Imagining Visual Affordances
patrickhaoy/ptp
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In effect, prior data is used to learn what kinds of outcomes may be possible, such that when the robot encounters an unfamiliar setting, it can sample potential outcomes from its model, attempt to reach them, and thereby update both its skills and its outcome model.
KaggleDBQA: Realistic Evaluation of Text-to-SQL Parsers
The goal of database question answering is to enable natural language querying of real-life relational databases in diverse application domains.
MetaMorph: Learning Universal Controllers with Transformers
agrimgupta92/metamorph
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ICLR 2022
Multiple domains like vision, natural language, and audio are witnessing tremendous progress by leveraging Transformers for large scale pre-training followed by task specific fine tuning.
BigBIO: A Framework for Data-Centric Biomedical Natural Language Processing
Training and evaluating language models increasingly requires the construction of meta-datasets --diverse collections of curated data with clear provenance.
