SOTAVerified

Transfer Learning

Transfer Learning is a machine learning technique where a model trained on one task is re-purposed and fine-tuned for a related, but different task. The idea behind transfer learning is to leverage the knowledge learned from a pre-trained model to solve a new, but related problem. This can be useful in situations where there is limited data available to train a new model from scratch, or when the new task is similar enough to the original task that the pre-trained model can be adapted to the new problem with only minor modifications.

( Image credit: Subodh Malgonde )

Papers

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Showing 8951–8975 of 10307 papers

TitleStatusHype
Bristle: Decentralized Federated Learning in Byzantine, Non-i.i.d. Environments—0
Buildings Classification using Very High Resolution Satellite Imagery—0
Bridging The Multi-Modality Gaps of Audio, Visual and Linguistic for Speech Enhancement—0
Bures Joint Distribution Alignment with Dynamic Margin for Unsupervised Domain Adaptation—0
Burgers' pinns with implicit euler transfer learning—0
Bridging the Gap: Transfer Learning from English PLMs to Malaysian English—0
Bypassing Optimization Complexity through Transfer Learning & Deep Neural Nets for Speech Intelligibility Improvement—0
C2KD: Bridging the Modality Gap for Cross-Modal Knowledge Distillation—0
CACTUS: An Open Dataset and Framework for Automated Cardiac Assessment and Classification of Ultrasound Images Using Deep Transfer Learning—0
CactusNets: Layer Applicability as a Metric for Transfer Learning—0
Public Parking Spot Detection And Geo-localization Using Transfer Learning—0
PUFFIN: A Path-Unifying Feed-Forward Interfaced Network for Vapor Pressure Prediction—0
CAE-DFKD: Bridging the Transferability Gap in Data-Free Knowledge Distillation—0
CAKD: A Correlation-Aware Knowledge Distillation Framework Based on Decoupling Kullback-Leibler Divergence—0
Calibrate and Prune: Improving Reliability of Lottery Tickets Through Prediction Calibration—0
Calibrated and Robust Foundation Models for Vision-Language and Medical Image Tasks Under Distribution Shift—0
Bridging the Bosphorus: Advancing Turkish Large Language Models through Strategies for Low-Resource Language Adaptation and Benchmarking—0
Bridging Modalities: Enhancing Cross-Modality Hate Speech Detection with Few-Shot In-Context Learning—0
Bridging Industrial Expertise and XR with LLM-Powered Conversational Agents—0
CALLIC: Content Adaptive Learning for Lossless Image Compression—0
Calliffusion: Chinese Calligraphy Generation and Style Transfer with Diffusion Modeling—0
CamemBERT-bio: Leveraging Continual Pre-training for Cost-Effective Models on French Biomedical Data—0
Camera On-boarding for Person Re-identification using Hypothesis Transfer Learning—0
Camouflaged Variational Graph AutoEncoder against Attribute Inference Attacks for Cross-Domain Recommendation—0
Bridging Ears and Eyes: Analyzing Audio and Visual Large Language Models to Humans in Visible Sound Recognition and Reducing Their Sensory Gap via Cross-Modal Distillation—0
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All datasetsOffice-Home100 sleep nights of 8 caregiversBanglaLekha Isolated DatasetCOCO70KITTI Object Tracking Evaluation 2012Retinal Fundus MultiDisease Image Dataset (RFMiD)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2—Unverified
2DFA-ENTAccuracy69.2—Unverified
3DFA-SAFNAccuracy69.1—Unverified
4EasyTLAccuracy63.3—Unverified
5MEDAAccuracy60.3—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23—Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65—Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74—Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95—Unverified