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 701–725 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
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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