Search Results for author:
Found 11 papers, 2 papers with code
Reset & Distill: A Recipe for Overcoming Negative Transfer in Continual Reinforcement Learning
We argue that one of the main obstacles for developing effective Continual Reinforcement Learning (CRL) algorithms is the negative transfer issue occurring when the new task to learn arrives.
ACLS: Adaptive and Conditional Label Smoothing for Network Calibration
We present in this paper an in-depth analysis of existing regularization-based methods, providing a better understanding on how they affect to network calibration.
ALIFE: Adaptive Logit Regularizer and Feature Replay for Incremental Semantic Segmentation
Based on this, we then introduce an adaptive logit regularizer (ALI) that enables our model to better learn new categories, while retaining knowledge for previous ones.
Decomposed Knowledge Distillation for Class-Incremental Semantic Segmentation
We introduce a CISS framework that alleviates the forgetting problem and facilitates learning novel classes effectively.
Class-Incremental Semantic Segmentation
Knowledge Distillation
OIMNet++: Prototypical Normalization and Localization-aware Learning for Person Search
To this end, we introduce a novel normalization layer, dubbed ProtoNorm, that calibrates features from pedestrian proposals, while considering a long-tail distribution of person IDs, enabling L2 normalized person representations to be discriminative.
Model-augmented Prioritized Experience Replay
Experience replay is an essential component in off-policy model-free reinforcement learning (MfRL).
Exploiting a Joint Embedding Space for Generalized Zero-Shot Semantic Segmentation
To this end, we leverage visual and semantic encoders to learn a joint embedding space, where the semantic encoder transforms semantic features to semantic prototypes that act as centers for visual features of corresponding classes.
Background-Aware Pooling and Noise-Aware Loss for Weakly-Supervised Semantic Segmentation
We address the problem of weakly-supervised semantic segmentation (WSSS) using bounding box annotations.
Model-Augmented Q-learning
We show that the proposed scheme, called Model-augmented $Q$-learning (MQL), obtains a policy-invariant solution which is identical to the solution obtained by learning with true reward.
Learning to Sample with Local and Global Contexts in Experience Replay Buffer
Experience replay, which enables the agents to remember and reuse experience from the past, has played a significant role in the success of off-policy reinforcement learning (RL).
Training Deep Neural Network in Limited Precision
Energy and resource efficient training of DNNs will greatly extend the applications of deep learning.
