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Found 39 papers, 16 papers with code
One-stage Prompt-based Continual Learning
With the QR loss, our approach maintains ~ 50% computational cost reduction during inference as well as outperforms the prior two-stage PCL methods by ~1. 4% on public class-incremental continual learning benchmarks including CIFAR-100, ImageNet-R, and DomainNet.
TT-SNN: Tensor Train Decomposition for Efficient Spiking Neural Network Training
Spiking Neural Networks (SNNs) have gained significant attention as a potentially energy-efficient alternative for standard neural networks with their sparse binary activation.
GenQ: Quantization in Low Data Regimes with Generative Synthetic Data
In the realm of deep neural network deployment, low-bit quantization presents a promising avenue for enhancing computational efficiency.
Rethinking Skip Connections in Spiking Neural Networks with Time-To-First-Spike Coding
In this work, we delve into the role of skip connections, a widely used concept in Artificial Neural Networks (ANNs), within the domain of SNNs with TTFS coding.
RobustEdge: Low Power Adversarial Detection for Cloud-Edge Systems
However, in prior detection works, the detector is attached to the classifier model and both detector and classifier work in tandem to perform adversarial detection that requires a high computational overhead which is not available at the low-power edge.
Artificial to Spiking Neural Networks Conversion for Scientific Machine Learning
We introduce a method to convert Physics-Informed Neural Networks (PINNs), commonly used in scientific machine learning, to Spiking Neural Networks (SNNs), which are expected to have higher energy efficiency compared to traditional Artificial Neural Networks (ANNs).
Examining the Role and Limits of Batchnorm Optimization to Mitigate Diverse Hardware-noise in In-memory Computing
In-Memory Computing (IMC) platforms such as analog crossbars are gaining focus as they facilitate the acceleration of low-precision Deep Neural Networks (DNNs) with high area- & compute-efficiencies.
Sharing Leaky-Integrate-and-Fire Neurons for Memory-Efficient Spiking Neural Networks
Spiking Neural Networks (SNNs) have gained increasing attention as energy-efficient neural networks owing to their binary and asynchronous computation.
Do We Really Need a Large Number of Visual Prompts?
Due to increasing interest in adapting models on resource-constrained edges, parameter-efficient transfer learning has been widely explored.
Divide-and-Conquer the NAS puzzle in Resource Constrained Federated Learning Systems
In this paper, we propose DC-NAS -- a divide-and-conquer approach that performs supernet-based Neural Architecture Search (NAS) in a federated system by systematically sampling the search space.
Uncovering the Representation of Spiking Neural Networks Trained with Surrogate Gradient
However, some essential questions exist pertaining to SNNs that are little studied: Do SNNs trained with surrogate gradient learn different representations from traditional Artificial Neural Networks (ANNs)?
SEENN: Towards Temporal Spiking Early-Exit Neural Networks
However, we observe that the information capacity in SNNs is affected by the number of timesteps, leading to an accuracy-efficiency tradeoff.
XPert: Peripheral Circuit & Neural Architecture Co-search for Area and Energy-efficient Xbar-based Computing
The hardware-efficiency and accuracy of Deep Neural Networks (DNNs) implemented on In-memory Computing (IMC) architectures primarily depend on the DNN architecture and the peripheral circuit parameters.
Workload-Balanced Pruning for Sparse Spiking Neural Networks
Though the existing pruning methods can provide extremely high weight sparsity for deep SNNs, the high weight sparsity brings a workload imbalance problem.
Exploring Temporal Information Dynamics in Spiking Neural Networks
After training, we observe that information becomes highly concentrated in earlier few timesteps, a phenomenon we refer to as temporal information concentration.
Wearable-based Human Activity Recognition with Spatio-Temporal Spiking Neural Networks
SNNs allow spatio-temporal extraction of features and enjoy low-power computation with binary spikes.
Exploring Lottery Ticket Hypothesis in Spiking Neural Networks
To scale up a pruning technique towards deep SNNs, we investigate Lottery Ticket Hypothesis (LTH) which states that dense networks contain smaller subnetworks (i. e., winning tickets) that achieve comparable performance to the dense networks.
Examining the Robustness of Spiking Neural Networks on Non-ideal Memristive Crossbars
Spiking Neural Networks (SNNs) have recently emerged as the low-power alternative to Artificial Neural Networks (ANNs) owing to their asynchronous, sparse, and binary information processing.
Loss-based Sequential Learning for Active Domain Adaptation
To solve these, we fully utilize pseudo labels of the unlabeled target domain by leveraging loss prediction.
SATA: Sparsity-Aware Training Accelerator for Spiking Neural Networks
Based on SATA, we show quantitative analyses of the energy efficiency of SNN training and compare the training cost of SNNs and ANNs.
Addressing Client Drift in Federated Continual Learning with Adaptive Optimization
However, there is little attention towards additional challenges emerging when federated aggregation is performed in a continual learning system.
Neuromorphic Data Augmentation for Training Spiking Neural Networks
In an effort to minimize this generalization gap, we propose Neuromorphic Data Augmentation (NDA), a family of geometric augmentations specifically designed for event-based datasets with the goal of significantly stabilizing the SNN training and reducing the generalization gap between training and test performance.
Ranked #1 on
Event data classification
on CIFAR10-DVS
(using extra training data)
Adversarial Detection without Model Information
We train a standalone detector independent of the classifier model, with a layer-wise energy separation (LES) training to increase the separation between natural and adversarial energies.
Rate Coding or Direct Coding: Which One is Better for Accurate, Robust, and Energy-efficient Spiking Neural Networks?
Then, we measure the robustness of the coding techniques on two adversarial attack methods.
Neural Architecture Search for Spiking Neural Networks
Interestingly, SNASNet found by our search algorithm achieves higher performance with backward connections, demonstrating the importance of designing SNN architecture for suitably using temporal information.
Gradient-based Bit Encoding Optimization for Noise-Robust Binary Memristive Crossbar
In addition, we propose Gradient-based Bit Encoding Optimization (GBO) which optimizes a different number of pulses at each layer, based on our in-depth analysis that each layer has a different level of noise sensitivity.
Beyond Classification: Directly Training Spiking Neural Networks for Semantic Segmentation
Specifically, we first investigate two representative SNN optimization techniques for recognition tasks (i. e., ANN-SNN conversion and surrogate gradient learning) on semantic segmentation datasets.
Federated Learning with Spiking Neural Networks
To validate the proposed federated learning framework, we experimentally evaluate the advantages of SNNs on various aspects of federated learning with CIFAR10 and CIFAR100 benchmarks.
PrivateSNN: Privacy-Preserving Spiking Neural Networks
2) Class leakage is caused when class-related features can be reconstructed from network parameters.
Visual Explanations from Spiking Neural Networks using Interspike Intervals
Spiking Neural Networks (SNNs) compute and communicate with asynchronous binary temporal events that can lead to significant energy savings with neuromorphic hardware.
Revisiting Batch Normalization for Training Low-latency Deep Spiking Neural Networks from Scratch
Different from previous works, our proposed BNTT decouples the parameters in a BNTT layer along the time axis to capture the temporal dynamics of spikes.
Associative Partial Domain Adaptation
Our Associative Partial Domain Adaptation (APDA) utilizes intra-domain association to actively select out non-trivial anomaly samples in each source-private class that sample-level weighting cannot handle.
Domain Adaptation without Source Data
Our key idea is to leverage a pre-trained model from the source domain and progressively update the target model in a self-learning manner.
Partial Domain Adaptation Using Graph Convolutional Networks
Although existing partial domain adaptation methods effectively down-weigh outliers' importance, they do not consider data structure of each domain and do not directly align the feature distributions of the same class in the source and target domains, which may lead to misalignment of category-level distributions.
Hi-CMD: Hierarchical Cross-Modality Disentanglement for Visible-Infrared Person Re-Identification
To implement our approach, we introduce an ID-preserving person image generation network and a hierarchical feature learning module.
Combinational Class Activation Maps for Weakly Supervised Object Localization
Most previous methods utilize the activation map corresponding to the highest activation source.
CNN-based Semantic Segmentation using Level Set Loss
Note that each binary image consists of background and regions belonging to a class.
Learning to Align Multi-Camera Domains using Part-Aware Clustering for Unsupervised Video Person Re-Identification
Since the cost of labeling increases dramatically as the number of cameras increases, it is difficult to apply the re-identification algorithm to a large camera network.
RPM-Net: Robust Pixel-Level Matching Networks for Self-Supervised Video Object Segmentation
In this paper, we introduce a self-supervised approach for video object segmentation without human labeled data. Specifically, we present Robust Pixel-level Matching Net-works (RPM-Net), a novel deep architecture that matches pixels between adjacent frames, using only color information from unlabeled videos for training.
