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 5576–5600 of 10307 papers

TitleStatusHype
SHuBERT: Self-Supervised Sign Language Representation Learning via Multi-Stream Cluster Prediction—0
Shuffle Augmentation of Features from Unlabeled Data for Unsupervised Domain Adaptation—0
Shuffle Vision Transformer: Lightweight, Fast and Efficient Recognition of Driver Facial Expression—0
Siamese Content Loss Networks for Highly Imbalanced Medical Image Segmentation—0
Distance Metric-Based Learning with Interpolated Latent Features for Location Classification in Endoscopy Image and Video—0
Sign Language Recognition System using TensorFlow Object Detection API—0
Sign Language to Text Conversion in Real Time using Transfer Learning—0
SIGTYP 2020 Shared Task: Prediction of Typological Features—0
Silhouette: Toward Performance-Conscious and Transferable CPU Embeddings—0
Siloed Federated Learning for Multi-Centric Histopathology Datasets—0
Sim2real transfer learning for 3D human pose estimation: motion to the rescue—0
Sim2real Transfer Learning for Point Cloud Segmentation: An Industrial Application Case on Autonomous Disassembly—0
Sim2SG: Sim-to-Real Scene Graph Generation for Transfer Learning—0
Self-Supervised Real-to-Sim Scene Generation—0
SimDA: Simple Diffusion Adapter for Efficient Video Generation—0
Similarit\'e s\'emantique entre phrases : apprentissage par transfert interlingue (Semantic Sentence Similarity : Multilingual Transfer Learning)—0
Similarity-based Knowledge Transfer for Cross-Domain Reinforcement Learning—0
Similarity-Based Reconstruction Loss for Meaning Representation—0
Similarity-based transfer learning of decision policies—0
Similarity metrics for Different Market Scenarios in Abides—0
Similarity of Pre-trained and Fine-tuned Representations—0
Simple and Effective Transfer Learning for Neuro-Symbolic Integration—0
Simple Control Baselines for Evaluating Transfer Learning—0
SimpleMTOD: A Simple Language Model for Multimodal Task-Oriented Dialogue with Symbolic Scene Representation—0
Simple Semantic Annotation and Situation Frames: Two Approaches to Basic Text Understanding in LORELEI—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