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Found 14 papers, 4 papers with code
Human Gaze Boosts Object-Centered Representation Learning
To account for the importance of central vision in humans, we crop the visual area around the gaze location.
Seeing the Whole in the Parts in Self-Supervised Representation Learning
Here, we propose a new way to model spatial co-occurrences by aligning local representations (before pooling) with a global image representation.
Active Gaze Behavior Boosts Self-Supervised Object Learning
In this work, we explore whether a bio inspired visual learning model can harness toddlers' gaze behavior during a play session to develop view-invariant object recognition.
Self-supervised visual learning from interactions with objects
In our analysis, we find that the observed improvement is associated with a better viewpoint-wise alignment of different objects from the same category.
Learning Object Semantic Similarity with Self-Supervision
To achieve this, the model exploits two distinct strategies: the visuo-language alignment ensures that different objects of the same category are represented similarly, whereas the temporal alignment leverages that objects from the same context are frequently seen in succession to make their representations more similar.
Self-Supervised Learning of Color Constancy
Color constancy (CC) describes the ability of the visual system to perceive an object as having a relatively constant color despite changes in lighting conditions.
MIMo: A Multi-Modal Infant Model for Studying Cognitive Development
Human intelligence and human consciousness emerge gradually during the process of cognitive development.
Caregiver Talk Shapes Toddler Vision: A Computational Study of Dyadic Play
For this we propose a computational model of visual representation learning during dyadic play.
An information-theoretic perspective on intrinsic motivation in reinforcement learning: a survey
The reinforcement learning (RL) research area is very active, with an important number of new contributions; especially considering the emergent field of deep RL (DRL).
Time to augment self-supervised visual representation learning
Specifically, our analyses reveal that: 1) 3-D object manipulations drastically improve the learning of object categories; 2) viewing objects against changing backgrounds is important for learning to discard background-related information from the latent representation.
Embodied vision for learning object representations
During each play session the agent views an object in multiple orientations before turning its body to view another object.
DisTop: Discovering a Topological representation to learn diverse and rewarding skills
The optimal way for a deep reinforcement learning (DRL) agent to explore is to learn a set of skills that achieves a uniform distribution of states.
Deep Reinforcement Learning
Hierarchical Reinforcement Learning
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ELSIM: End-to-end learning of reusable skills through intrinsic motivation
Then we show that our approach can scale on more difficult MuJoCo environments in which our agent is able to build a representation of skills which improve over a baseline both transfer learning and exploration when rewards are sparse.
A survey on intrinsic motivation in reinforcement learning
In this article, we provide a survey on the role of intrinsic motivation in DRL.
