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 251–300 of 1356 papers

TitleStatusHype
Canonical convolutional neural networksCode0
RanDeS: Randomized Delta Superposition for Multi-Model CompressionCode0
On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep LearningCode0
Compressed Object DetectionCode0
LilNetX: Lightweight Networks with EXtreme Model Compression and Structured SparsificationCode0
MedDet: Generative Adversarial Distillation for Efficient Cervical Disc Herniation DetectionCode0
Learning Intrinsic Sparse Structures within Long Short-Term MemoryCode0
Learning Deep and Compact Models for Gesture RecognitionCode0
Learning Compression from Limited Unlabeled DataCode0
Learning Efficient Detector with Semi-supervised Adaptive DistillationCode0
Large Multimodal Model Compression via Efficient Pruning and Distillation at AntGroupCode0
Class-dependent Compression of Deep Neural NetworksCode0
Boosting Large Language Models with Mask Fine-TuningCode0
Language Model Knowledge Distillation for Efficient Question Answering in SpanishCode0
Learning Accurate Performance Predictors for Ultrafast Automated Model CompressionCode0
Towards Efficient Model Compression via Learned Global RankingCode0
Knowledge Distillation with Reptile Meta-Learning for Pretrained Language Model CompressionCode0
StructADMM: A Systematic, High-Efficiency Framework of Structured Weight Pruning for DNNsCode0
Knowledge Grafting of Large Language ModelsCode0
Knowledge Distillation as Semiparametric InferenceCode0
Knowledge Distillation for End-to-End Person SearchCode0
Binary Classification as a Phase Separation ProcessCode0
Accelerating and Compressing Deep Neural Networks for Massive MIMO CSI FeedbackCode0
Knowledge Distillation for Singing Voice DetectionCode0
Knowledge Translation: A New Pathway for Model CompressionCode0
Paraphrasing Complex Network: Network Compression via Factor TransferCode0
Is Modularity Transferable? A Case Study through the Lens of Knowledge DistillationCode0
InDistill: Information flow-preserving knowledge distillation for model compressionCode0
Beyond Perplexity: Multi-dimensional Safety Evaluation of LLM CompressionCode0
Information-Theoretic Understanding of Population Risk Improvement with Model CompressionCode0
Iterative Filter Pruning for Concatenation-based CNN ArchitecturesCode0
Actor-Mimic: Deep Multitask and Transfer Reinforcement LearningCode0
JavaScript Convolutional Neural Networks for Keyword Spotting in the Browser: An Experimental AnalysisCode0
Bayesian Tensorized Neural Networks with Automatic Rank SelectionCode0
I3D: Transformer architectures with input-dependent dynamic depth for speech recognitionCode0
HTR-JAND: Handwritten Text Recognition with Joint Attention Network and Knowledge DistillationCode0
HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model CompressionCode0
Hybrid Binary Networks: Optimizing for Accuracy, Efficiency and MemoryCode0
Image Classification with CondenseNeXt for ARM-Based Computing PlatformsCode0
How does topology of neural architectures impact gradient propagation and model performance?Code0
High-fidelity 3D Model Compression based on Key SpheresCode0
A Miniaturized Semantic Segmentation Method for Remote Sensing ImageCode0
ImPart: Importance-Aware Delta-Sparsification for Improved Model Compression and Merging in LLMsCode0
Gradual Channel Pruning while Training using Feature Relevance Scores for Convolutional Neural NetworksCode0
GSB: Group Superposition Binarization for Vision Transformer with Limited Training SamplesCode0
A Brief Review of Hypernetworks in Deep LearningCode0
AutoMC: Automated Model Compression based on Domain Knowledge and Progressive search strategyCode0
Generalizing Teacher Networks for Effective Knowledge Distillation Across Student ArchitecturesCode0
GASL: Guided Attention for Sparsity Learning in Deep Neural NetworksCode0
From Dense to Sparse: Contrastive Pruning for Better Pre-trained Language Model CompressionCode0
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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