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Found 22 papers, 7 papers with code
A Broader Study of Cross-Domain Few-Shot Learning
Extensive experiments on the proposed benchmark are performed to evaluate state-of-art meta-learning approaches, transfer learning approaches, and newer methods for cross-domain few-shot learning.
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Cross-Domain Few-Shot
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cross-domain few-shot learning
Few-Shot Image Classification
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SpotTune: Transfer Learning through Adaptive Fine-tuning
Transfer learning, which allows a source task to affect the inductive bias of the target task, is widely used in computer vision.
Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networks
To fully unleash the potential of Async-HFL in converging speed under system heterogeneities and stragglers, we design device selection at the gateway level and device-gateway association at the cloud level.
Improved Schemes for Episodic Memory-based Lifelong Learning
This view leads to two improved schemes for episodic memory based lifelong learning, called MEGA-I and MEGA-II.
Depthwise Convolution is All You Need for Learning Multiple Visual Domains
A model aware of the relationships between different domains can also be trained to work on new domains with less resources.
SCALE: Online Self-Supervised Lifelong Learning without Prior Knowledge
To address the challenges, we propose Self-Supervised ContrAstive Lifelong LEarning without Prior Knowledge (SCALE) which can extract and memorize representations on the fly purely from the data continuum.
RAPIDNN: In-Memory Deep Neural Network Acceleration Framework
To enable in-memory processing, RAPIDNN reinterprets a DNN model and maps it into a specialized accelerator, which is designed using non-volatile memory blocks that model four fundamental DNN operations, i. e., multiplication, addition, activation functions, and pooling.
AdaFilter: Adaptive Filter Fine-tuning for Deep Transfer Learning
Fine-tuning is a popular transfer learning technique for deep neural networks where a few rounds of training are applied to the parameters of a pre-trained model to adapt them to a new task.
Attacking Lifelong Learning Models with Gradient Reversion
We evaluate the effectiveness of traditional attack methods such as FGSM and PGD. The results show that A-GEM still possesses strong continual learning ability in the presence of adversarial examples in the memory and simple defense techniques such as label smoothing can further alleviate the adversarial effects.
Prive-HD: Privacy-Preserved Hyperdimensional Computing
In this paper, we target privacy-preserving training and inference of brain-inspired Hyperdimensional (HD) computing, a new learning algorithm that is gaining traction due to its light-weight computation and robustness particularly appealing for edge devices with tight constraints.
SHEARer: Highly-Efficient Hyperdimensional Computing by Software-Hardware Enabled Multifold Approximation
In this paper, we propose SHEARer, an algorithm-hardware co-optimization to improve the performance and energy consumption of HD computing.
A Theoretical Perspective on Hyperdimensional Computing
Hyperdimensional (HD) computing is a set of neurally inspired methods for obtaining high-dimensional, low-precision, distributed representations of data.
FPGA Acceleration of Sequence Alignment: A Survey
In this survey, we introduce three main DNA alignment algorithms and FPGA-based implementation of these algorithms to accelerate the DNA alignment.
Learning with Long-term Remembering: Following the Lead of Mixed Stochastic Gradient
In this paper, we introduce a novel and effective lifelong learning algorithm, called MixEd stochastic GrAdient (MEGA), which allows deep neural networks to acquire the ability of retaining performance on old tasks while learning new tasks.
RES-HD: Resilient Intelligent Fault Diagnosis Against Adversarial Attacks Using Hyper-Dimensional Computing
Hyper-dimensional computing (HDC) is a brain-inspired machine learning method that has been shown to be sufficiently accurate while being extremely robust, fast, and energy-efficient.
Streaming Encoding Algorithms for Scalable Hyperdimensional Computing
Hyperdimensional computing (HDC) is a paradigm for data representation and learning originating in computational neuroscience.
DODEM: DOuble DEfense Mechanism Against Adversarial Attacks Towards Secure Industrial Internet of Things Analytics
The results show that our double defense strategy is highly efficient where we can improve model robustness by up to 64. 6% and 52% compared to standard and adversarial retraining, respectively.
Mem-Rec: Memory Efficient Recommendation System using Alternative Representation
Deep learning-based recommendation systems (e. g., DLRMs) are widely used AI models to provide high-quality personalized recommendations.
SpecHD: Hyperdimensional Computing Framework for FPGA-based Mass Spectrometry Clustering
This approach markedly improves clustering speed and efficiency, serving as a catalyst for real-time, high-throughput data analysis in future healthcare applications.
Federated Hyperdimensional Computing
The strategies we propose to improve the communication efficiency enable our design to reduce communication costs by 66$\times$ vs. DNNs, local client compute and energy consumption by ~1. 5 - 6$\times$, while being highly robust to network errors.
Lifelong Intelligence Beyond the Edge using Hyperdimensional Computing
On-device learning has emerged as a prevailing trend that avoids the slow response time and costly communication of cloud-based learning.
MicroHD: An Accuracy-Driven Optimization of Hyperdimensional Computing Algorithms for TinyML systems
Hyperdimensional computing (HDC) is emerging as a promising AI approach that can effectively target TinyML applications thanks to its lightweight computing and memory requirements.
