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 1776–1800 of 10307 papers

TitleStatusHype
From Large to Super-Tiny: End-to-End Optimization for Cost-Efficient LLMs—0
MetaDSE: A Few-shot Meta-learning Framework for Cross-workload CPU Design Space Exploration—0
Feature Alignment and Representation Transfer in Knowledge Distillation for Large Language Models—0
Convergence and Implicit Bias of Gradient Descent on Continual Linear Classification—0
Transfer Learning via Auxiliary Labels with Application to Cold-Hardiness Prediction—0
Quantum Computing Supported Adversarial Attack-Resilient Autonomous Vehicle Perception Module for Traffic Sign ClassificationCode0
Pandora: A Code-Driven Large Language Model Agent for Unified Reasoning Across Diverse Structured Knowledge—0
Scaling Laws for Data-Efficient Visual Transfer Learning—0
Enhancing Cocoa Pod Disease Classification via Transfer Learning and Ensemble Methods: Toward Robust Predictive Modeling—0
Non-Uniform Class-Wise Coreset Selection: Characterizing Category Difficulty for Data-Efficient Transfer Learning—0
Secure Transfer Learning: Training Clean Models Against Backdoor in (Both) Pre-trained Encoders and Downstream Datasets—0
3D-PointZshotS: Geometry-Aware 3D Point Cloud Zero-Shot Semantic Segmentation Narrowing the Visual-Semantic GapCode0
Meta-learning For Few-Shot Time Series Crop Type Classification: A Benchmark On The EuroCropsML DatasetCode0
Transfer Learning for Temporal Link PredictionCode0
Towards a Universal Vibration Analysis Dataset: A Framework for Transfer Learning in Predictive Maintenance and Structural Health Monitoring—0
Enhancing LLM-based Recommendation through Semantic-Aligned Collaborative Knowledge—0
Progressive Transfer Learning for Multi-Pass Fundus Image Restoration—0
Transfer Learning Assisted XgBoost For Adaptable Cyberattack Detection In Battery Packs—0
The Impact of Model Zoo Size and Composition on Weight Space LearningCode0
UP-Person: Unified Parameter-Efficient Transfer Learning for Text-based Person RetrievalCode0
Self-Controlled Dynamic Expansion Model for Continual Learning—0
Learning to Harmonize Cross-vendor X-ray Images by Non-linear Image Dynamics Correction—0
Inferring genotype-phenotype maps using attention modelsCode0
Beyond Glucose-Only Assessment: Advancing Nocturnal Hypoglycemia Prediction in Children with Type 1 Diabetes—0
Towards On-Device Learning and Reconfigurable Hardware Implementation for Encoded Single-Photon Signal Processing—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