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 251–275 of 10307 papers

TitleStatusHype
Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning—0
π_0.5: a Vision-Language-Action Model with Open-World Generalization—0
SPECI: Skill Prompts based Hierarchical Continual Imitation Learning for Robot Manipulation—0
Transfer Learning for High-dimensional Reduced Rank Time Series Models—0
Research on Cloud Platform Network Traffic Monitoring and Anomaly Detection System based on Large Language Models—0
Fourier analysis of the physics of transfer learning for data-driven subgrid-scale models of ocean turbulence—0
Is Intelligence the Right Direction in New OS Scheduling for Multiple Resources in Cloud Environments?—0
PIV-FlowDiffuser:Transfer-learning-based denoising diffusion models for PIVCode0
Histogram-based Parameter-efficient Tuning for Passive Sonar ClassificationCode0
Turbo2K: Towards Ultra-Efficient and High-Quality 2K Video Synthesis—0
Empirical Evaluation of Knowledge Distillation from Transformers to Subquadratic Language Models—0
CLIP-Powered Domain Generalization and Domain Adaptation: A Comprehensive SurveyCode2
From Large to Super-Tiny: End-to-End Optimization for Cost-Efficient LLMs—0
Enhancing Pothole Detection and Characterization: Integrated Segmentation and Depth Estimation in Road Anomaly Systems—0
Feature Alignment and Representation Transfer in Knowledge Distillation for Large Language Models—0
A Deep Learning-Based Supervised Transfer Learning Framework for DOA Estimation with Array ImperfectionsCode1
MetaDSE: A Few-shot Meta-learning Framework for Cross-workload CPU Design Space Exploration—0
CheXWorld: Exploring Image World Modeling for Radiograph Representation LearningCode1
Enhancing Cocoa Pod Disease Classification via Transfer Learning and Ensemble Methods: Toward Robust Predictive Modeling—0
Transfer Learning via Auxiliary Labels with Application to Cold-Hardiness Prediction—0
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
Convergence and Implicit Bias of Gradient Descent on Continual Linear Classification—0
Non-Uniform Class-Wise Coreset Selection: Characterizing Category Difficulty for Data-Efficient Transfer Learning—0
Quantum Computing Supported Adversarial Attack-Resilient Autonomous Vehicle Perception Module for Traffic Sign ClassificationCode0
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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