Papers Binarization
“Binarization” 태그가 달린 논문 433편 · 필터 해제
A Sample Efficient Conditional Independence Test in the Presence of Discretization
In many real-world scenarios, interested variables are often represented as discretized values due to measurement limitations. Applying Conditional Independence (CI) tests directly to such discretized data, however, can …
BinarizationHighly Efficient and Effective LLMs with Multi-Boolean Architectures
Weight binarization has emerged as a promising strategy to drastically reduce the complexity of large language models (LLMs). It is mainly classified into two approaches: post-training binarization and finetuning with tr…
BinarizationQuantizationRoBiS: Robust Binary Segmentation for High-Resolution Industrial Images
Robust unsupervised anomaly detection (AD) in real-world scenarios is an important task. Current methods exhibit severe performance degradation on the MVTec AD 2 benchmark due to its complex real-world challenges. To sol…
Anomaly DetectionBinarizationData AugmentationUnsupervised Anomaly DetectionAddition is almost all you need: Compressing neural networks with double binary factorization
Binary quantization approaches, which replace weight matrices with binary matrices and substitute costly multiplications with cheaper additions, offer a computationally efficient approach to address the increasing comput…
AllBinarizationQuantizationPhysics-guided and fabrication-aware inverse design of photonic devices using diffusion models
Designing free-form photonic devices is fundamentally challenging due to the vast number of possible geometries and the complex requirements of fabrication constraints. Traditional inverse-design approaches--whether driv…
BinarizationDenoisingglobal-optimizationExpanding-and-Shrinking Binary Neural Networks
While binary neural networks (BNNs) offer significant benefits in terms of speed, memory and energy, they encounter substantial accuracy degradation in challenging tasks compared to their real-valued counterparts. Due to…
Binarizationimage-classificationImage Classificationobject-detection+1Partial differential equation system for binarization of degraded document images
In recent years, partial differential equation (PDE) systems have been successfully applied to the binarization of text images, achieving promising results. Inspired by the DH model and incorporating a novel image modeli…
BinarizationBHViT: Binarized Hybrid Vision Transformer
Model binarization has made significant progress in enabling real-time and energy-efficient computation for convolutional neural networks (CNN), offering a potential solution to the deployment challenges faced by Vision …
BinarizationQuantization10K is Enough: An Ultra-Lightweight Binarized Network for Infrared Small-Target Detection
The widespread deployment of InfRared Small-Target Detection(IRSTD) algorithms on edge devices necessitates the exploration of model compression techniques. Binary neural networks (BNNs) are distinguished by their except…
BinarizationModel CompressionTowards Accurate Binary Spiking Neural Networks: Learning with Adaptive Gradient Modulation Mechanism
Binary Spiking Neural Networks (BSNNs) inherit the eventdriven paradigm of SNNs, while also adopting the reduced storage burden of binarization techniques. These distinct advantages grant BSNNs lightweight and energy-eff…
BinarizationPTQ1.61: Push the Real Limit of Extremely Low-Bit Post-Training Quantization Methods for Large Language Models
Large Language Models (LLMs) suffer severe performance degradation when facing extremely low-bit (sub 2-bit) quantization. Several existing sub 2-bit post-training quantization (PTQ) methods utilize a mix-precision schem…
BinarizationQuantizationSparse Autoencoder Features for Classifications and Transferability
Sparse Autoencoders (SAEs) provide potentials for uncovering structured, human-interpretable representations in Large Language Models (LLMs), making them a crucial tool for transparent and controllable AI systems. We sys…
Binarizationfeature selectionDeep Learning-based Event Data Coding: A Joint Spatiotemporal and Polarity Solution
Neuromorphic vision sensors, commonly referred to as event cameras, have recently gained relevance for applications requiring high-speed, high dynamic range and low-latency data acquisition. Unlike traditional frame-base…
BinarizationHamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers
Pre-trained transformer models with extended context windows are notoriously expensive to run at scale, often limiting real-world deployment due to their high computational and memory requirements. In this paper, we intr…
BinarizationProgressive Binarization with Semi-Structured Pruning for LLMs
Large language models (LLMs) have achieved remarkable success in natural language processing tasks, but their high computational and memory demands pose challenges for deployment on resource-constrained devices. Binariza…
BinarizationBiMaCoSR: Binary One-Step Diffusion Model Leveraging Flexible Matrix Compression for Real Super-Resolution
While super-resolution (SR) methods based on diffusion models (DM) have demonstrated inspiring performance, their deployment is impeded due to the heavy request of memory and computation. Recent researchers apply two kin…
BinarizationSuper-ResolutionImproving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters
There has been significant focus on creating neuro-symbolic models for interpretable image classification using Convolutional Neural Networks (CNNs). These methods aim to replace the CNN with a neuro-symbolic model consi…
Binarizationimage-classificationImage ClassificationHadamRNN: Binary and Sparse Ternary Orthogonal RNNs
Binary and sparse ternary weights in neural networks enable faster computations and lighter representations, facilitating their use on edge devices with limited computational power. Meanwhile, vanilla RNNs are highly sen…
BinarizationPfungst and Clever Hans: Identifying the unintended cues in a widely used Alzheimer's disease MRI dataset using explainable deep learning
Backgrounds. Deep neural networks have demonstrated high accuracy in classifying Alzheimer's disease (AD). This study aims to enlighten the underlying black-box nature and reveal individual contributions of T1-weighted (…
BinarizationSkull StrippingSpecificityExplaining Categorical Feature Interactions Using Graph Covariance and LLMs
Modern datasets often consist of numerous samples with abundant features and associated timestamps. Analyzing such datasets to uncover underlying events typically requires complex statistical methods and substantial doma…
Binarization