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 976–1000 of 1356 papers

TitleStatusHype
Towards Superior Quantization Accuracy: A Layer-sensitive Approach—0
A Low-Power Streaming Speech Enhancement Accelerator For Edge Devices—0
Performance Aware Convolutional Neural Network Channel Pruning for Embedded GPUs—0
PERMDNN: Efficient Compressed DNN Architecture with Permuted Diagonal Matrices—0
Perturbation of Deep Autoencoder Weights for Model Compression and Classification of Tabular Data—0
PFGDF: Pruning Filter via Gaussian Distribution Feature for Deep Neural Networks Acceleration—0
Weight Squeezing: Reparameterization for Knowledge Transfer and Model Compression—0
Pivoting Factorization: A Compact Meta Low-Rank Representation of Sparsity for Efficient Inference in Large Language Models—0
A Low Effort Approach to Structured CNN Design Using PCA—0
Do we need Label Regularization to Fine-tune Pre-trained Language Models?—0
Tensor Train Low-rank Approximation (TT-LoRA): Democratizing AI with Accelerated LLMs—0
A Lottery Ticket Hypothesis Framework for Low-Complexity Device-Robust Neural Acoustic Scene Classification—0
Towards Zero-Shot Knowledge Distillation for Natural Language Processing—0
Robustness Challenges in Model Distillation and Pruning for Natural Language Understanding—0
Position-Aware Depth Decay Decoding (D^3): Boosting Large Language Model Inference Efficiency—0
Aligned Weight Regularizers for Pruning Pretrained Neural Networks—0
Post-Training Quantization for Video Matting—0
Shedding the Bits: Pushing the Boundaries of Quantization with Minifloats on FPGAs—0
Post-Training Weighted Quantization of Neural Networks for Language Models—0
PQK: Model Compression via Pruning, Quantization, and Knowledge Distillation—0
Practical quantum federated learning and its experimental demonstration—0
Precise Box Score: Extract More Information from Datasets to Improve the Performance of Face Detection—0
What do larger image classifiers memorise?—0
Preventing Catastrophic Forgetting and Distribution Mismatch in Knowledge Distillation via Synthetic Data—0
Preview-based Category Contrastive Learning for Knowledge Distillation—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