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Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning
TLDR
In this paper we propose a self-adaptive visual navigation method (SAVN) which learns to adapt to new environments without any explicit supervision. Expand
SeGAN: Segmenting and Generating the Invisible
TLDR
We propose SeGAN, a novel GAN-based model that combines segmentation and generation and jointly optimizes for both of them. Expand
Watching the World Go By: Representation Learning from Unlabeled Videos
TLDR
We propose Video Noise Contrastive Estimation, a method for using unlabeled video to learn strong, transferable single image representations, across a variety of temporal and non-temporal tasks. Expand
Use the Force, Luke! Learning to Predict Physical Forces by Simulating Effects
TLDR
We address the problem of inferring contact points and the physical forces from videos of humans interacting with objects. Expand
Who Let the Dogs Out? Modeling Dog Behavior from Visual Data
TLDR
We study the task of directly modelling a visually intelligent agent. Expand
Learning Generalizable Visual Representations via Interactive Gameplay
TLDR
We show that embodied adversarial reinforcement learning agents playing cache, a variant of hide-and-seek, in a high fidelity, interactive, environment, learn representations of their observations encoding information such as occlusion, object permanence, free space, and containment; on par with representations learnt by the most popular modern paradigm for visual representation learning which requires large datasets independently labeled for each new task. Expand
Orientations of graphs avoiding given lists on out-degrees
TLDR
In this paper it is shown that if G is bipartite and |F (v)| ≤ dG(v) 2 for every v ∈ V (G), then G is F -avoiding. Expand
What Can You Learn from Your Muscles? Learning Visual Representation from Human Interactions
TLDR
Learning effective representations of visual data that generalize to a variety of downstream tasks has been a long quest for computer vision. Expand
Act the Part: Learning Interaction Strategies for Articulated Object Part Discovery
TLDR
We introduce Act the Part (AtP) to learn how to interact with articulated objects to discover and segment their pieces. Expand
Contrasting Contrastive Self-Supervised Representation Learning Models
TLDR
In the past few years, we have witnessed remarkable breakthroughs in self-supervised representation learning. Expand
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