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Found 9 papers, 3 papers with code
CAM-loss: Towards Learning Spatially Discriminative Feature Representations
The backbone of traditional CNN classifier is generally considered as a feature extractor, followed by a linear layer which performs the classification.
Fine-Grained Few Shot Learning with Foreground Object Transformation
As a data augmentation method, FOT can be conveniently applied to any existing few shot learning algorithm and greatly improve its performance on FG-FSL tasks.
Efficient Knowledge Distillation from Model Checkpoints
Knowledge distillation is an effective approach to learn compact models (students) with the supervision of large and strong models (teachers).
Boosting Offline Reinforcement Learning with Action Preference Query
In particular, online fine-tuning has become a commonly used method to correct the erroneous estimates of out-of-distribution data learned in the offline training phase.
Decoupled Prioritized Resampling for Offline RL
OPER is a plug-and-play component for offline RL algorithms.
Hundreds Guide Millions: Adaptive Offline Reinforcement Learning with Expert Guidance
Offline reinforcement learning (RL) optimizes the policy on a previously collected dataset without any interactions with the environment, yet usually suffers from the distributional shift problem.
Avalon's Game of Thoughts: Battle Against Deception through Recursive Contemplation
This study utilizes the intricate Avalon game as a testbed to explore LLMs' potential in deceptive environments.
Train Once, Get a Family: State-Adaptive Balances for Offline-to-Online Reinforcement Learning
Existing solutions tackle this problem by imposing a policy constraint on the policy improvement objective in both offline and online learning.
LLM Agents for Psychology: A Study on Gamified Assessments
Psychological measurement is essential for mental health, self-understanding, and personal development.
