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 8301–8325 of 10307 papers

TitleStatusHype
Understanding Transfer Learning for Chest Radiograph Clinical Report Generation with Modified Transformer Architectures—0
Understanding Transfer Learning via Mean-field Analysis—0
Understanding Unconventional Preprocessors in Deep Convolutional Neural Networks for Face Identification—0
Understanding WiFi Signal Frequency Features for Position-Independent Gesture Sensing—0
Understand the Implication: Learning to Think for Pragmatic Understanding—0
Under the Hood of Neural Networks: Characterizing Learned Representations by Functional Neuron Populations and Network Ablations—0
Underwater Acoustic Communication Channel Modeling using Reservoir Computing—0
UniCal: a Single-Branch Transformer-Based Model for Camera-to-LiDAR Calibration and Validation—0
Unified and Effective Ensemble Knowledge Distillation—0
A Unified Training Process for Fake News Detection based on Fine-Tuned BERT Model—0
Unified HT-CNNs Architecture: Transfer Learning for Segmenting Diverse Brain Tumors in MRI from Gliomas to Pediatric Tumors—0
Unified Humor Detection Based on Sentence-pair Augmentation and Transfer Learning—0
Unified Locomotion Transformer with Simultaneous Sim-to-Real Transfer for Quadrupeds—0
Unified Principles For Multi-Source Transfer Learning Under Label Shifts—0
Unified Representation Learning for Efficient Medical Image Analysis—0
Unified Transfer Learning Models in High-Dimensional Linear Regression—0
Unifying Direct and Indirect Learning for Safe Control of Linear Systems—0
EMatch: A Unified Framework for Event-based Optical Flow and Stereo Matching—0
Uni-Removal: A Semi-Supervised Framework for Simultaneously Addressing Multiple Degradations in Real-World Images—0
Unity is Power: Semi-Asynchronous Collaborative Training of Large-Scale Models with Structured Pruning in Resource-Limited Clients—0
Universal Adaptive Control of Nonlinear Systems—0
Universal Feature Selection for Simultaneous Interpretability of Multitask Datasets—0
Universality in Transfer Learning for Linear Models—0
Universal Lesion Detection by Learning from Multiple Heterogeneously Labeled Datasets—0
Learning Unified Distance Metric Across Diverse Data Distributions with Parameter-Efficient Transfer Learning—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