Zero-shot Generalization
255 papers with code • 1 benchmarks • 1 datasets
CALVIN
Most implemented papers
Multitask Prompted Training Enables Zero-Shot Task Generalization
Large language models have recently been shown to attain reasonable zero-shot generalization on a diverse set of tasks (Brown et al., 2020).
Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks
Humans can understand and produce new utterances effortlessly, thanks to their compositional skills.
ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth
isl-org/ZoeDepth
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Finally, ZoeD-M12-NK is the first model that can jointly train on multiple datasets (NYU Depth v2 and KITTI) without a significant drop in performance and achieve unprecedented zero-shot generalization performance to eight unseen datasets from both indoor and outdoor domains.
Learning Transferable Cooperative Behavior in Multi-Agent Teams
sumitsk/matrl
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While multi-agent interactions can be naturally modeled as a graph, the environment has traditionally been considered as a black box.
Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset
google-research-datasets/dstc8-schema-guided-dialogue
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In this work, we introduce the the Schema-Guided Dialogue (SGD) dataset, containing over 16k multi-domain conversations spanning 16 domains.
Learning the Travelling Salesperson Problem Requires Rethinking Generalization
End-to-end training of neural network solvers for graph combinatorial optimization problems such as the Travelling Salesperson Problem (TSP) have seen a surge of interest recently, but remain intractable and inefficient beyond graphs with few hundreds of nodes.
Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation
prs-eth/marigold
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CVPR 2024
Monocular depth estimation is a fundamental computer vision task.
Convolutional Conditional Neural Processes
cambridge-mlg/convcnp
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ICLR 2020
We introduce the Convolutional Conditional Neural Process (ConvCNP), a new member of the Neural Process family that models translation equivariance in the data.
Compositional Generalization with Tree Stack Memory Units
We study compositional generalization, viz., the problem of zero-shot generalization to novel compositions of concepts in a domain.
The Scattering Compositional Learner: Discovering Objects, Attributes, Relationships in Analogical Reasoning
dhh1995/SCL
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In this work, we focus on an analogical reasoning task that contains rich compositional structures, Raven's Progressive Matrices (RPM).
