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Found 15 papers, 5 papers with code
Exploiting Style Latent Flows for Generalizing Deepfake Detection Video Detection
This paper presents a new approach for the detection of fake videos, based on the analysis of style latent vectors and their abnormal behavior in temporal changes in the generated videos.
Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning
As a result, it is usually necessary to reproduce the experiments from scratch, which can be time-consuming and error-prone.
Fast Inference and Transfer of Compositional Task Structures for Few-shot Task Generalization
Different from the previous meta-rl methods trying to directly infer the unstructured task embedding, our multi-task subtask graph inferencer (MTSGI) first infers the common high-level task structure in terms of the subtask graph from the training tasks, and use it as a prior to improve the task inference in testing.
Hierarchical Reinforcement Learning
Meta Reinforcement Learning
+2
Lipschitz-constrained Unsupervised Skill Discovery
To address this issue, we propose Lipschitz-constrained Skill Discovery (LSD), which encourages the agent to discover more diverse, dynamic, and far-reaching skills.
Environment Generation for Zero-Shot Compositional Reinforcement Learning
We learn to generate environments composed of multiple pages or rooms, and train RL agents capable of completing wide-range of complex tasks in those environments.
Successor Feature Landmarks for Long-Horizon Goal-Conditioned Reinforcement Learning
SFL leverages the ability of successor features (SF) to capture transition dynamics, using it to drive exploration by estimating state-novelty and to enable high-level planning by abstracting the state-space as a non-parametric landmark-based graph.
Shortest-Path Constrained Reinforcement Learning for Sparse Reward Tasks
We propose the k-Shortest-Path (k-SP) constraint: a novel constraint on the agent's trajectory that improves the sample efficiency in sparse-reward MDPs.
Variational Empowerment as Representation Learning for Goal-Based Reinforcement Learning
Learning to reach goal states and learning diverse skills through mutual information (MI) maximization have been proposed as principled frameworks for self-supervised reinforcement learning, allowing agents to acquire broadly applicable multitask policies with minimal reward engineering.
Meta Reinforcement Learning with Autonomous Inference of Subtask Dependencies
We propose and address a novel few-shot RL problem, where a task is characterized by a subtask graph which describes a set of subtasks and their dependencies that are unknown to the agent.
Self-Imitation Learning via Trajectory-Conditioned Policy for Hard-Exploration Tasks
We propose a new method of learning a trajectory-conditioned policy to imitate diverse trajectories from the agent's own past experiences and show that such self-imitation helps avoid myopic behavior and increases the chance of finding a globally optimal solution for hard-exploration tasks, especially when there are misleading rewards.
Memory Based Trajectory-conditioned Policies for Learning from Sparse Rewards
Reinforcement learning with sparse rewards is challenging because an agent can rarely obtain non-zero rewards and hence, gradient-based optimization of parameterized policies can be incremental and slow.
Contingency-Aware Exploration in Reinforcement Learning
This paper investigates whether learning contingency-awareness and controllable aspects of an environment can lead to better exploration in reinforcement learning.
Ranked #8 on
Atari Games
on Atari 2600 Montezuma's Revenge
Inferring Semantic Layout for Hierarchical Text-to-Image Synthesis
We propose a novel hierarchical approach for text-to-image synthesis by inferring semantic layout.
Supervising Neural Attention Models for Video Captioning by Human Gaze Data
The attention mechanisms in deep neural networks are inspired by human's attention that sequentially focuses on the most relevant parts of the information over time to generate prediction output.
End-to-end Concept Word Detection for Video Captioning, Retrieval, and Question Answering
We propose a high-level concept word detector that can be integrated with any video-to-language models.
Ranked #37 on
Video Retrieval
on LSMDC
