Quantization
10개 벤치마크 · 논문 4,925편 · 이 태스크의 논문 보기 →
Benchmarks
ImageNet
CIFAR-10
AgeDB-30
CFP-FP
IJB-B
IJB-C
Knowledge-based:
LFW
Wiki-40B
Most implemented
FastText.zip: Compressing text classification models
wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
QLoRA: Efficient Finetuning of Quantized LLMs
Papers
Efficient Deployment of Spiking Neural Networks on SpiNNaker2 for DVS Gesture Recognition Using Neuromorphic Intermediate Representation
Spiking Neural Networks (SNNs) are highly energy-efficient during inference, making them particularly suitable for deployment on neuromorphic hardware. Their ability to process event-driven inputs, such as data from dyna…
Edge-computingGesture RecognitionQuantizationAn End-to-End DNN Inference Framework for the SpiNNaker2 Neuromorphic MPSoC
This work presents a multi-layer DNN scheduling framework as an extension of OctopuScheduler, providing an end-to-end flow from PyTorch models to inference on a single SpiNNaker2 chip. Together with a front-end comprised…
QuantizationSchedulingTask-Specific Audio Coding for Machines: Machine-Learned Latent Features Are Codes for That Machine
Neural audio codecs, leveraging quantization algorithms, have significantly impacted various speech/audio tasks. While high-fidelity reconstruction is paramount for human perception, audio coding for machines (ACoM) prio…
Audio ClassificationAutomatic Speech RecognitionQuantizationspeech-recognition+1Angle Estimation of a Single Source with Massive Uniform Circular Arrays
Estimating the directions of arrival (DOAs) of incoming plane waves is an essential topic in array signal processing. Widely adopted uniform linear arrays can only provide estimates of source azimuth. Thus, uniform circu…
QuantizationQuantized Rank Reduction: A Communications-Efficient Federated Learning Scheme for Network-Critical Applications
Federated learning is a machine learning approach that enables multiple devices (i.e., agents) to train a shared model cooperatively without exchanging raw data. This technique keeps data localized on user devices, ensur…
Federated LearningQuantizationMGVQ: Could VQ-VAE Beat VAE? A Generalizable Tokenizer with Multi-group Quantization
Vector Quantized Variational Autoencoders (VQ-VAEs) are fundamental models that compress continuous visual data into discrete tokens. Existing methods have tried to improve the quantization strategy for better reconstruc…
2kImage GenerationImage ReconstructionQuantization