SOTAVerified

Model Compression

Model Compression is an actively pursued area of research over the last few years with the goal of deploying state-of-the-art deep networks in low-power and resource limited devices without significant drop in accuracy. Parameter pruning, low-rank factorization and weight quantization are some of the proposed methods to compress the size of deep networks.

Source: KD-MRI: A knowledge distillation framework for image reconstruction and image restoration in MRI workflow

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 401–450 of 1356 papers

TitleStatusHype
Does Learning Require Memorization? A Short Tale about a Long Tail—0
Domain Adaptation Regularization for Spectral Pruning—0
Domain Generalization on Efficient Acoustic Scene Classification using Residual Normalization—0
Don't Be So Dense: Sparse-to-Sparse GAN Training Without Sacrificing Performance—0
Don't encrypt the data; just approximate the model \ Towards Secure Transaction and Fair Pricing of Training Data—0
Double Viterbi: Weight Encoding for High Compression Ratio and Fast On-Chip Reconstruction for Deep Neural Network—0
Enhancing Targeted Attack Transferability via Diversified Weight Pruning—0
Dream Distillation: A Data-Independent Model Compression Framework—0
Dreaming To Prune Image Deraining Networks—0
Stochastic Model Pruning via Weight Dropping Away and Back—0
Decoupling Weight Regularization from Batch Size for Model Compression—0
Debiased Distillation by Transplanting the Last Layer—0
Automatic Block-wise Pruning with Auxiliary Gating Structures for Deep Convolutional Neural Networks—0
Dual sparse training framework: inducing activation map sparsity via Transformed 1 regularization—0
Can Model Compression Improve NLP Fairness—0
Dynamically Hierarchy Revolution: DirNet for Compressing Recurrent Neural Network on Mobile Devices—0
Data-Model-Circuit Tri-Design for Ultra-Light Video Intelligence on Edge Devices—0
Data-Independent Structured Pruning of Neural Networks via Coresets—0
Dynamic Model Pruning with Feedback—0
Dynamic Probabilistic Pruning: Training sparse networks based on stochastic and dynamic masking—0
Can Students Outperform Teachers in Knowledge Distillation based Model Compression?—0
Automated Model Compression by Jointly Applied Pruning and Quantization—0
Dynamic Sparse Learning: A Novel Paradigm for Efficient Recommendation—0
DynaQuant: Compressing Deep Learning Training Checkpoints via Dynamic Quantization—0
Cascaded channel pruning using hierarchical self-distillation—0
AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models—0
ECoFLaP: Efficient Coarse-to-Fine Layer-Wise Pruning for Vision-Language Models—0
EDCompress: Energy-Aware Model Compression for Dataflows—0
Edge AI: Evaluation of Model Compression Techniques for Convolutional Neural Networks—0
Edge-AI for Agriculture: Lightweight Vision Models for Disease Detection in Resource-Limited Settings—0
Edge Deep Learning for Neural Implants—0
Edge-First Language Model Inference: Models, Metrics, and Tradeoffs—0
Edge-MultiAI: Multi-Tenancy of Latency-Sensitive Deep Learning Applications on Edge—0
Edge-Optimized Deep Learning & Pattern Recognition Techniques for Non-Intrusive Load Monitoring of Energy Time Series—0
Data-Free Quantization via Pseudo-label Filtering—0
Effective and Efficient Mixed Precision Quantization of Speech Foundation Models—0
Effective and Efficient One-pass Compression of Speech Foundation Models Using Sparsity-aware Self-pinching Gates—0
Effective Interplay between Sparsity and Quantization: From Theory to Practice—0
Effective Multi-Stage Training Model For Edge Computing Devices In Intrusion Detection—0
Efficiency optimization of large-scale language models based on deep learning in natural language processing tasks—0
Efficient AI in Practice: Training and Deployment of Efficient LLMs for Industry Applications—0
Adaptive Learning of Tensor Network Structures—0
Data-Free Quantization via Mixed-Precision Compensation without Fine-Tuning—0
Efficient Apple Maturity and Damage Assessment: A Lightweight Detection Model with GAN and Attention Mechanism—0
Automated Inference of Graph Transformation Rules—0
Efficient classification using parallel and scalable compressed model and Its application on intrusion detection—0
Efficient Computation of Quantized Neural Networks by −1, +1 Encoding Decomposition—0
Data-Free Knowledge Transfer: A Survey—0
Efficient DNN-Powered Software with Fair Sparse Models—0
Auto Graph Encoder-Decoder for Neural Network Pruning—0
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All datasetsImageNetQNLI

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ADLIK-MO-ResNet50+W4A4Top-177.88—Unverified
2ADLIK-MO-ResNet50+W3A4Top-177.34—Unverified
3ResNet-18 + 4bit-1dim model compression using DKMTop-170.52—Unverified
4MobileNet-v1 + 4bit-1dim model compression using DKMTop-169.63—Unverified
5ResNet-18 + 2bit-1dim model compression using DKMTop-168.63—Unverified
6MobileNet-v1 + 2bit-1dim model compression using DKMTop-167.62—Unverified
7ResNet-18 + 4bit-4dim model compression using DKMTop-166.1—Unverified
8ResNet-18 + 2bit-2dim model compression using DKMTop-164.7—Unverified
9MobileNet-v1 + 4bit-4dim model compression using DKMTop-161.4—Unverified
10ResNet-18 + 1bit-1dim model compression using DKMTop-159.7—Unverified
#ModelMetricClaimedVerifiedStatus
1MobileBERT + 2bit-1dim model compression using DKMAccuracy82.13—Unverified
2MobileBERT + 1bit-1dim model compression using DKMAccuracy63.17—Unverified