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 2301–2325 of 10307 papers

TitleStatusHype
CoT-Driven Framework for Short Text Classification: Enhancing and Transferring Capabilities from Large to Smaller Model—0
Efficient Bitrate Ladder Construction using Transfer Learning and Spatio-Temporal FeaturesCode0
Offline Handwriting Signature Verification: A Transfer Learning and Feature Selection Approach—0
Physics-Informed Neural Networks for High-Frequency and Multi-Scale Problems using Transfer Learning—0
Detection and Classification of Diabetic Retinopathy using Deep Learning Algorithms for Segmentation to Facilitate Referral Recommendation for Test and Treatment PredictionCode1
GTA: Guided Transfer of Spatial Attention from Object-Centric Representations—0
Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes InteractivelyCode5
Nurse-in-the-Loop Artificial Intelligence for Precision Management of Type 2 Diabetes in a Clinical Trial Utilizing Transfer-Learned Predictive Digital Twin—0
Multi-Source Domain Adaptation with Transformer-based Feature Generation for Subject-Independent EEG-based Emotion Recognition—0
Graph Neural Networks for Surfactant Multi-Property PredictionCode0
Towards Multi-Objective High-Dimensional Feature Selection via Evolutionary Multitasking—0
The Power of Training: How Different Neural Network Setups Influence the Energy Demand—0
GBSS:a global building semantic segmentation dataset for large-scale remote sensing building extraction—0
DTBS: Dual-Teacher Bi-directional Self-training for Domain Adaptation in Nighttime Semantic SegmentationCode1
CommonCanvas: Open Diffusion Models Trained on Creative-Commons Images—0
Structured Model Probing: Empowering Efficient Transfer Learning by Structured Regularization—0
Discriminative Pattern Calibration Mechanism for Source-Free Domain Adaptation—0
Exploring Region-Word Alignment in Built-in Detector for Open-Vocabulary Object Detection—0
Enhanced Motion-Text Alignment for Image-to-Video Transfer Learning—0
Semantics Distortion and Style Matter: Towards Source-free UDA for Panoramic Segmentation—0
Scene-adaptive and Region-aware Multi-modal Prompt for Open Vocabulary Object Detection—0
Distribution-aware Knowledge Prototyping for Non-exemplar Lifelong Person Re-identificationCode1
Evaluating Transferability in Retrieval Tasks: An Approach Using MMD and Kernel Methods—0
C2KD: Bridging the Modality Gap for Cross-Modal Knowledge Distillation—0
MMA: Multi-Modal Adapter for Vision-Language ModelsCode2
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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