Papers Data Free Quantization
“Data Free Quantization” 태그가 달린 논문 37편 · 필터 해제
Zero-Shot Learning of a Conditional Generative Adversarial Network for Data-Free Network Quantization
We propose a novel method for training a conditional generative adversarial network (CGAN) without the use of training data, called zero-shot learning of a CGAN (ZS-CGAN). Zero-shot learning of a conditional generator on…
Data Free QuantizationGenerative Adversarial NetworkQuantizationZero-Shot LearningPSAQ-ViT V2: Towards Accurate and General Data-Free Quantization for Vision Transformers
Data-free quantization can potentially address data privacy and security concerns in model compression, and thus has been widely investigated. Recently, PSAQ-ViT designs a relative value metric, patch similarity, to gene…
Data Free Quantizationimage-classificationImage ClassificationModel Compression+4Towards Feature Distribution Alignment and Diversity Enhancement for Data-Free Quantization
To obtain lower inference latency and less memory footprint of deep neural networks, model quantization has been widely employed in deep model deployment, by converting the floating points to low-precision integers. Howe…
Data Free QuantizationDiversityModel CompressionQuantization+1Data-Free Quantization with Accurate Activation Clipping and Adaptive Batch Normalization
Data-free quantization is a task that compresses the neural network to low bit-width without access to original training data. Most existing data-free quantization methods cause severe performance degradation due to inac…
Data Free QuantizationQuantizationIt's All In the Teacher: Zero-Shot Quantization Brought Closer to the Teacher
Model quantization is considered as a promising method to greatly reduce the resource requirements of deep neural networks. To deal with the performance drop induced by quantization errors, a popular method is to use tra…
AllData Free QuantizationKnowledge DistillationQuantizationSPIQ: Data-Free Per-Channel Static Input Quantization
Computationally expensive neural networks are ubiquitous in computer vision and solutions for efficient inference have drawn a growing attention in the machine learning community. Examples of such solutions comprise quan…
Data Free Quantizationobject-detectionObject DetectionQuantization+1Patch Similarity Aware Data-Free Quantization for Vision Transformers
Vision transformers have recently gained great success on various computer vision tasks; nevertheless, their high model complexity makes it challenging to deploy on resource-constrained devices. Quantization is an effect…
Data Free QuantizationQuantizationSQuant: On-the-Fly Data-Free Quantization via Diagonal Hessian Approximation
Quantization of deep neural networks (DNN) has been proven effective for compressing and accelerating DNN models. Data-free quantization (DFQ) is a promising approach without the original datasets under privacy-sensitive…
Data Free QuantizationQuantizationQimera: Data-free Quantization with Synthetic Boundary Supporting Samples
Model quantization is known as a promising method to compress deep neural networks, especially for inferences on lightweight mobile or edge devices. However, model quantization usually requires access to the original tra…
Data Free QuantizationDisentanglementDiversityQuantizationDiverse Sample Generation: Pushing the Limit of Generative Data-free Quantization
Generative data-free quantization emerges as a practical compression approach that quantizes deep neural networks to low bit-width without accessing the real data. This approach generates data utilizing batch normalizati…
Data Free Quantizationimage-classificationImage ClassificationQuantizationZero-shot Adversarial Quantization
Model quantization is a promising approach to compress deep neural networks and accelerate inference, making it possible to be deployed on mobile and edge devices. To retain the high performance of full-precision models,…
Data Free QuantizationQuantizationTransfer LearningDiversifying Sample Generation for Accurate Data-Free Quantization
Quantization has emerged as one of the most prevalent approaches to compress and accelerate neural networks. Recently, data-free quantization has been widely studied as a practical and promising solution. It synthesizes …
Data Free Quantizationimage-classificationImage ClassificationQuantizationGenerative Zero-shot Network Quantization
Convolutional neural networks are able to learn realistic image priors from numerous training samples in low-level image generation and restoration. We show that, for high-level image recognition tasks, we can further re…
Data Free QuantizationImage GenerationQuantizationTowards Accurate Quantization and Pruning via Data-free Knowledge Transfer
When large scale training data is available, one can obtain compact and accurate networks to be deployed in resource-constrained environments effectively through quantization and pruning. However, training data are often…
Data Free QuantizationQuantizationTransfer LearningGenerative Low-bitwidth Data Free Quantization
Neural network quantization is an effective way to compress deep models and improve their execution latency and energy efficiency, so that they can be deployed on mobile or embedded devices. Existing quantization methods…
Data Free QuantizationQuantizationZeroQ: A Novel Zero Shot Quantization Framework
Quantization is a promising approach for reducing the inference time and memory footprint of neural networks. However, most existing quantization methods require access to the original training dataset for retraining dur…
Data Free QuantizationModel CompressionNeural Network CompressionQuantizationData-Free Quantization Through Weight Equalization and Bias Correction
We introduce a data-free quantization method for deep neural networks that does not require fine-tuning or hyperparameter selection. It achieves near-original model performance on common computer vision architectures and…
Data Free Quantizationobject-detectionObject DetectionQuantization+1