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Contrasting Contrastive Self-Supervised Representation Learning Pipelines
TLDR
This paper analyzes contrastive approaches as one of the most successful and popular variants of self-supervised representation learning and examines over 700 training experiments including 30 encoders, 4 pre-training datasets and 20 diverse downstream tasks.
AllenAct: A Framework for Embodied AI Research
TLDR
AllenAct is introduced, a modular and flexible learning framework designed with a focus on the unique requirements of Embodied AI research that provides first-class support for a growing collection of embodied environments, tasks and algorithms.
Contrasting Contrastive Self-Supervised Representation Learning Models
TLDR
This paper analyzes contrastive approaches as one of the most successful and popular variants of self-supervised representation learning and examines over 700 training experiments including 30 encoders, 4 pre-training datasets and 20 diverse downstream tasks.
Interactron: Embodied Adaptive Object Detection
TLDR
The idea is to continue training during inference and adapt the model at test time without any explicit supervision via interacting with the environment, and its performance is on par with a model trained with full supervision for those environments.