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Found 22 papers, 8 papers with code
MTLight: Efficient Multi-Task Reinforcement Learning for Traffic Signal Control
Traffic signal control has a great impact on alleviating traffic congestion in modern cities.
Noisy Spiking Actor Network for Exploration
As a general method for exploration in deep reinforcement learning (RL), NoisyNet can produce problem-specific exploration strategies.
Adaptive Discovering and Merging for Incremental Novel Class Discovery
One important desideratum of lifelong learning aims to discover novel classes from unlabelled data in a continuous manner.
Fully Spiking Actor Network with Intra-layer Connections for Reinforcement Learning
Recently, the surrogate gradient method has been utilized for training multi-layer SNNs, which allows SNNs to achieve comparable performance with the corresponding deep networks in this task.
Learning Sparse Neural Networks with Identity Layers
Applying such theory, we propose a plug-and-play CKA-based Sparsity Regularization for sparse network training, dubbed CKA-SR, which utilizes CKA to reduce feature similarity between layers and increase network sparsity.
Population-Based Evolutionary Gaming for Unsupervised Person Re-identification
Extensive experiments demonstrate that (1) CRS approximately measures the performance of models without labeled samples; (2) and PEG produces new state-of-the-art accuracy for person re-identification, indicating the great potential of population-based network cooperative training for unsupervised learning.
Knowledge Distillation
Unsupervised Person Re-Identification
Picking Up Quantization Steps for Compressed Image Classification
The sensitivity of deep neural networks to compressed images hinders their usage in many real applications, which means classification networks may fail just after taking a screenshot and saving it as a compressed file.
Learning with Fantasy: Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental Learning
However, in this work, we find that the CE loss is not ideal for the base session training as it suffers poor class separation in terms of representations, which further degrades generalization to novel classes.
Training Full Spike Neural Networks via Auxiliary Accumulation Pathway
The accumulation in AAP could compensate for the information loss during the forward and backward of full spike propagation, and facilitate the training of the FSNN.
Simoun: Synergizing Interactive Motion-appearance Understanding for Vision-based Reinforcement Learning
Efficient motion and appearance modeling are critical for vision-based Reinforcement Learning (RL).
Stabilizing Visual Reinforcement Learning via Asymmetric Interactive Cooperation
Vision-based reinforcement learning (RL) depends on discriminative representation encoders to abstract the observation states.
Annotation Efficient Person Re-Identification with Diverse Cluster-Based Pair Selection
To solve this problem, we propose the Annotation Efficient Person Re-Identification method to select image pairs from an alternative pair set according to the fallibility and diversity of pairs, and train the Re-ID model based on the annotation.
Deep Reinforcement Learning with Spiking Q-learning
With the help of special neuromorphic hardware, spiking neural networks (SNNs) are expected to realize artificial intelligence (AI) with less energy consumption.
Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency Domain
This observation leads to more explanations of the CNN's generalization behaviors in both robustness to common perturbations and out-of-distribution detection, and motivates a new perspective on data augmentation designed by re-combing the phase spectrum of the current image and the amplitude spectrum of the distracter image.
Ranked #7 on
Out-of-Distribution Detection
on CIFAR-10
Adversarial Reciprocal Points Learning for Open Set Recognition
Then, an adversarial margin constraint is proposed to reduce the open space risk by limiting the latent open space constructed by reciprocal points.
MetaVIM: Meta Variationally Intrinsic Motivated Reinforcement Learning for Decentralized Traffic Signal Control
To make the policy learned from a training scenario generalizable to new unseen scenarios, a novel Meta Variationally Intrinsic Motivated (MetaVIM) RL method is proposed to learn the decentralized policy for each intersection that considers neighbor information in a latent way.
Learning Open Set Network with Discriminative Reciprocal Points
In this process, one of the key challenges is to reduce the risk of generalizing the inherent characteristics of numerous unknown samples learned from a small amount of known data.
Masked Face Recognition with Latent Part Detection
The proposed LPD model is trained in an end-to-end manner and only utilizes the original and synthetic training data.
Domain Adaptive Attention Model for Unsupervised Cross-Domain Person Re-Identification
To reduce domain divergence caused by that the source and target datasets are collected from different environments, we force to project the DSH feature maps from different domains to a new nominal domain, and a novel domain similarity loss is proposed based on one-class classification.
Cooperative Multi-Agent Policy Gradients with Sub-optimal Demonstration
To learn the multi-agent cooperation effectively and tackle the sub-optimality of demonstration, a self-improving learning method is proposed: On the one hand, the centralized state-action values are initialized by the demonstration and updated by the learned decentralized policy to improve the sub-optimality.
Visual Tracking via Spatially Aligned Correlation Filters Network
Correlation filters based trackers rely on a periodic assumption of the search sample to efficiently distinguish the target from the background.
Unsupervised Cross-Dataset Transfer Learning for Person Re-Identification
Most existing person re-identification (Re-ID) approaches follow a supervised learning framework, in which a large number of labelled matching pairs are required for training.
