Search Results for author:
Found 9 papers, 5 papers with code
VLM-PL: Advanced Pseudo Labeling approach Class Incremental Object Detection with Vision-Language Model
This technique uses Vision-Language Model (VLM) to verify the correctness of pseudo ground-truths (GTs) without requiring additional model training.
SDDGR: Stable Diffusion-based Deep Generative Replay for Class Incremental Object Detection
In the field of class incremental learning (CIL), genera- tive replay has become increasingly prominent as a method to mitigate the catastrophic forgetting, alongside the con- tinuous improvements in generative models.
Class Incremental Learning
Class-Incremental Object Detection
+3
Class-Wise Buffer Management for Incremental Object Detection: An Effective Buffer Training Strategy
In this paper, we introduce an effective buffer training strategy (eBTS) that creates the optimized replay buffer on object detection.
Imitating Graph-Based Planning with Goal-Conditioned Policies
Recently, graph-based planning algorithms have gained much attention to solve goal-conditioned reinforcement learning (RL) tasks: they provide a sequence of subgoals to reach the target-goal, and the agents learn to execute subgoal-conditioned policies.
Multi-View Masked World Models for Visual Robotic Manipulation
In this paper, we investigate how to learn good representations with multi-view data and utilize them for visual robotic manipulation.
Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning
In this paper, we present HIerarchical reinforcement learning Guided by Landmarks (HIGL), a novel framework for training a high-level policy with a reduced action space guided by landmarks, i. e., promising states to explore.
Efficient Exploration
Hierarchical Reinforcement Learning
+2
Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning
Although these two sources are both important factors for learning robust policies of agents, prior works do not separate them or deal with only a single risk source, which could lead to suboptimal equilibria.
Ranked #1 on
SMAC+
on Off_Near_parallel
Multi-agent Reinforcement Learning
reinforcement-learning
+3
Self-Improved Retrosynthetic Planning
Our main idea is based on a self-improving procedure that trains the model to imitate successful trajectories found by itself.
Ranked #4 on
Multi-step retrosynthesis
on USPTO-190
Guiding Deep Molecular Optimization with Genetic Exploration
De novo molecular design attempts to search over the chemical space for molecules with the desired property.
