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 701–750 of 1356 papers

TitleStatusHype
Knowledge Distillation with Multi-granularity Mixture of Priors for Image Super-Resolution—0
A Short Study on Compressing Decoder-Based Language Models—0
Representative Teacher Keys for Knowledge Distillation Model Compression Based on Attention Mechanism for Image Classification—0
The Lottery LLM Hypothesis, Rethinking What Abilities Should LLM Compression Preserve?—0
Accelerating Machine Learning Primitives on Commodity Hardware—0
ASER: Activation Smoothing and Error Reconstruction for Large Language Model Quantization—0
Theoretical Guarantees for Low-Rank Compression of Deep Neural Networks—0
Know What You Don't Need: Single-Shot Meta-Pruning for Attention Heads—0
KroneckerBERT: Learning Kronecker Decomposition for Pre-trained Language Models via Knowledge Distillation—0
KroneckerBERT: Significant Compression of Pre-trained Language Models Through Kronecker Decomposition and Knowledge Distillation—0
Kronecker Decomposition for GPT Compression—0
L4Q: Parameter Efficient Quantization-Aware Fine-Tuning on Large Language Models—0
LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression—0
Language model compression with weighted low-rank factorization—0
The Potential of AutoML for Recommender Systems—0
Large Language Model Compression via the Nested Activation-Aware Decomposition—0
Wasserstein Contrastive Representation Distillation—0
Large receptive field strategy and important feature extraction strategy in 3D object detection—0
Large-Scale Generative Data-Free Distillation—0
LatentLLM: Attention-Aware Joint Tensor Compression—0
LayerCollapse: Adaptive compression of neural networks—0
Layer-specific Optimization for Mixed Data Flow with Mixed Precision in FPGA Design for CNN-based Object Detectors—0
LCQ: Low-Rank Codebook based Quantization for Large Language Models—0
A Selective Survey on Versatile Knowledge Distillation Paradigm for Neural Network Models—0
A Scale Mixture Perspective of Multiplicative Noise in Neural Networks—0
Watermarking Graph Neural Networks by Random Graphs—0
Learning a Neural Diff for Speech Models—0
Learning-Based Symbol Level Precoding: A Memory-Efficient Unsupervised Learning Approach—0
Learning Compressed Embeddings for On-Device Inference—0
Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity—0
Accelerating Inference and Language Model Fusion of Recurrent Neural Network Transducers via End-to-End 4-bit Quantization—0
Learning Disentangled Representation with Mutual Information Maximization for Real-Time UAV Tracking—0
Three Dimensional Convolutional Neural Network Pruning with Regularization-Based Method—0
Learning Efficient Image Super-Resolution Networks via Structure-Regularized Pruning—0
Learning Efficient Object Detection Models with Knowledge Distillation—0
ASCAI: Adaptive Sampling for acquiring Compact AI—0
Learning by Sampling and Compressing: Efficient Graph Representation Learning with Extremely Limited Annotations—0
MixMix: All You Need for Data-Free Compression Are Feature and Data Mixing—0
Learning Interpretation with Explainable Knowledge Distillation—0
WeClick: Weakly-Supervised Video Semantic Segmentation with Click Annotations—0
Learning Low-Rank Approximation for CNNs—0
Learning Low-Rank Representations for Model Compression—0
Model Compression Method for S4 with Diagonal State Space Layers using Balanced Truncation—0
Artemis: HE-Aware Training for Efficient Privacy-Preserving Machine Learning—0
Learning to Collide: Recommendation System Model Compression with Learned Hash Functions—0
Learning to Prune Deep Neural Networks via Reinforcement Learning—0
Legal-Tech Open Diaries: Lesson learned on how to develop and deploy light-weight models in the era of humongous Language Models—0
LegoDNN: Block-grained Scaling of Deep Neural Networks for Mobile Vision—0
Tight Compression: Compressing CNN Through Fine-Grained Pruning and Weight Permutation for Efficient Implementation—0
Are We There Yet? A Measurement Study of Efficiency for LLM Applications on Mobile Devices—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