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
SSDA: Secure Source-Free Domain AdaptationCode0
ICD-Face: Intra-class Compactness Distillation for Face Recognition—0
Memory-Friendly Scalable Super-Resolution via Rewinding Lottery Ticket Hypothesis—0
FlatENN: Train Flat for Enhanced Fault Tolerance of Quantized Deep Neural Networks—0
BD-KD: Balancing the Divergences for Online Knowledge Distillation—0
Spike encoding techniques for IoT time-varying signals benchmarked on a neuromorphic classification taskCode0
FSCNN: A Fast Sparse Convolution Neural Network Inference System—0
Swing Distillation: A Privacy-Preserving Knowledge Distillation Framework—0
Can We Find Strong Lottery Tickets in Generative Models?—0
Efficient Speech Representation Learning with Low-Bit Quantization—0
Error-aware Quantization through Noise Tempering—0
Leveraging Different Learning Styles for Improved Knowledge Distillation in Biomedical Imaging—0
CSTAR: Towards Compact and STructured Deep Neural Networks with Adversarial Robustness—0
GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning—0
Compressing Cross-Lingual Multi-Task Models at Qualtrics—0
Design and Prototyping Distributed CNN Inference Acceleration in Edge Computing—0
Learning Low-Rank Representations for Model Compression—0
Understanding and Improving Knowledge Distillation for Quantization-Aware Training of Large Transformer EncodersCode0
Is Smaller Always Faster? Tradeoffs in Compressing Self-Supervised Speech TransformersCode0
Edge-MultiAI: Multi-Tenancy of Latency-Sensitive Deep Learning Applications on Edge—0
Understanding the Role of Mixup in Knowledge Distillation: An Empirical StudyCode0
XAI-BayesHAR: A novel Framework for Human Activity Recognition with Integrated Uncertainty and Shapely Values—0
Model Compression for DNN-based Speaker Verification Using Weight Quantization—0
COST-EFF: Collaborative Optimization of Spatial and Temporal Efficiency with Slenderized Multi-exit Language ModelsCode0
Fast DistilBERT on CPUs—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