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Found 26 papers, 8 papers with code
Cascaded Local Implicit Transformer for Arbitrary-Scale Super-Resolution
Implicit neural representation has recently shown a promising ability in representing images with arbitrary resolutions.
Local Implicit Normalizing Flow for Arbitrary-Scale Image Super-Resolution
Flow-based methods have demonstrated promising results in addressing the ill-posed nature of super-resolution (SR) by learning the distribution of high-resolution (HR) images with the normalizing flow.
Vision based Virtual Guidance for Navigation
This paper explores the impact of virtual guidance on mid-level representation-based navigation, where an agent performs navigation tasks based solely on visual observations.
ELDA: Using Edges to Have an Edge on Semantic Segmentation Based UDA
Despite their effectiveness, using depth as domain invariant information in UDA tasks may lead to multiple issues, such as excessively high extraction costs and difficulties in achieving a reliable prediction quality.
On Investigating the Conservative Property of Score-Based Generative Models
Existing Score-based Generative Models (SGMs) can be categorized into constrained SGMs (CSGMs) or unconstrained SGMs (USGMs) according to their parameterization approaches.
Pixel-Wise Prediction based Visual Odometry via Uncertainty Estimation
This paper introduces pixel-wise prediction based visual odometry (PWVO), which is a dense prediction task that evaluates the values of translation and rotation for every pixel in its input observations.
Denoising Likelihood Score Matching for Conditional Score-based Data Generation
These methods facilitate the training procedure of conditional score models, as a mixture of scores can be separately estimated using a score model and a classifier.
Investigation of Factorized Optical Flows as Mid-Level Representations
In this paper, we introduce a new concept of incorporating factorized flow maps as mid-level representations, for bridging the perception and the control modules in modular learning based robotic frameworks.
Variable-Length Music Score Infilling via XLNet and Musically Specialized Positional Encoding
This paper proposes a new self-attention based model for music score infilling, i. e., to generate a polyphonic music sequence that fills in the gap between given past and future contexts.
Reducing the Deployment-Time Inference Control Costs of Deep Reinforcement Learning Agents via an Asymmetric Architecture
Deep reinforcement learning (DRL) has been demonstrated to provide promising results in several challenging decision making and control tasks.
Rethinking Ensemble-Distillation for Semantic Segmentation Based Unsupervised Domain Adaptation
Recent researches on unsupervised domain adaptation (UDA) have demonstrated that end-to-end ensemble learning frameworks serve as a compelling option for UDA tasks.
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
In fully cooperative multi-agent reinforcement learning (MARL) settings, the environments are highly stochastic due to the partial observability of each agent and the continuously changing policies of the other agents.
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Semantic Segmentation Based Unsupervised Domain Adaptation via Pseudo-Label Fusion
In this paper, we propose a pseudo label fusion framework (PLF), a learning framework developed to deal with the domain gap between a source domain and a target domain for performing semantic segmentation based UDA in the unseen target domain.
Toward Synergism in Macro Action Ensembles
Macro actions have been demonstrated to be beneficial for the learning processes of an agent.
Mixture of Step Returns in Bootstrapped DQN
Nevertheless, integrating step returns into a single target sacrifices the diversity of the advantages offered by different step return targets.
Reusability and Transferability of Macro Actions for Reinforcement Learning
However, by using a proper macro action, defined as a sequence of primitive actions, an agent is able to bypass intermediate states to a farther state and facilitate its learning procedure.
Exploration via Flow-Based Intrinsic Rewards
Exploration bonuses derived from the novelty of observations in an environment have become a popular approach to motivate exploration for reinforcement learning (RL) agents in the past few years.
Adversarial Exploration Strategy for Self-Supervised Imitation Learning
Our framework consists of a deep reinforcement learning (DRL) agent and an inverse dynamics model contesting with each other.
A Self-Supervised Method for Mapping Human Instructions to Robot Policies
In this paper, we propose a modular approach which separates the instruction-to-action mapping procedure into two separate stages.
Never Forget: Balancing Exploration and Exploitation via Learning Optical Flow
We propose to employ optical flow estimation errors to examine the novelty of new observations, such that agents are able to memorize and understand the visited states in a more comprehensive fashion.
Visual Relationship Prediction via Label Clustering and Incorporation of Depth Information
The additional depth prediction path supplements the relationship prediction model in a way that bounding boxes or segmentation masks are unable to deliver.
Adversarial Active Exploration for Inverse Dynamics Model Learning
Our framework consists of a deep reinforcement learning (DRL) agent and an inverse dynamics model contesting with each other.
Dynamic Video Segmentation Network
We explore the use of a decision network to adaptively assign different frame regions to different networks based on a metric called expected confidence score.
Diversity-Driven Exploration Strategy for Deep Reinforcement Learning
Efficient exploration remains a challenging research problem in reinforcement learning, especially when an environment contains large state spaces, deceptive local optima, or sparse rewards.
Virtual-to-Real: Learning to Control in Visual Semantic Segmentation
Collecting training data from the physical world is usually time-consuming and even dangerous for fragile robots, and thus, recent advances in robot learning advocate the use of simulators as the training platform.
A Deep Policy Inference Q-Network for Multi-Agent Systems
DPIQN incorporates the learned policy features as a hidden vector into its own deep Q-network (DQN), such that it is able to predict better Q values for the controllable agents than the state-of-the-art deep reinforcement learning models.
