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 1176–1200 of 1356 papers

TitleStatusHype
RemoteTrimmer: Adaptive Structural Pruning for Remote Sensing Image ClassificationCode0
A Programmable Approach to Neural Network CompressionCode0
Beyond Perplexity: Multi-dimensional Safety Evaluation of LLM CompressionCode0
How does topology of neural architectures impact gradient propagation and model performance?Code0
Compressing Convolutional Neural Networks via Factorized Convolutional FiltersCode0
Knowledge Distillation with Reptile Meta-Learning for Pretrained Language Model CompressionCode0
Knowledge Grafting of Large Language ModelsCode0
High-fidelity 3D Model Compression based on Key SpheresCode0
Knowledge Translation: A New Pathway for Model CompressionCode0
Bayesian Tensorized Neural Networks with Automatic Rank SelectionCode0
Uncovering the Hidden Cost of Model CompressionCode0
DyCE: Dynamically Configurable Exiting for Deep Learning Compression and Real-time ScalingCode0
On-Device Neural Language Model Based Word PredictionCode0
VecQ: Minimal Loss DNN Model Compression With Vectorized Weight QuantizationCode0
SSDA: Secure Source-Free Domain AdaptationCode0
How does topology influence gradient propagation and model performance of deep networks with DenseNet-type skip connections?Code0
Language Model Knowledge Distillation for Efficient Question Answering in SpanishCode0
Universal approximation and model compression for radial neural networksCode0
Large Multimodal Model Compression via Efficient Pruning and Distillation at AntGroupCode0
Resource Constrained Model Compression via Minimax Optimization for Spiking Neural NetworksCode0
Data Efficient Stagewise Knowledge DistillationCode0
Online Ensemble Model Compression using Knowledge DistillationCode0
Distilling Universal and Joint Knowledge for Cross-Domain Model Compression on Time Series DataCode0
Compressed Object DetectionCode0
The Shallow End: Empowering Shallower Deep-Convolutional Networks through Auxiliary OutputsCode0
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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