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 51–75 of 10307 papers

TitleStatusHype
An Effective End-to-End Solution for Multimodal Action Recognition—0
The Sample Complexity of Online Strategic Decision Making with Information Asymmetry and Knowledge Transportability—0
Learning to Collaborate Over Graphs: A Selective Federated Multi-Task Learning ApproachCode0
An Explainable Deep Learning Framework for Brain Stroke and Tumor Progression via MRI Interpretation—0
Efficient Learning of Vehicle Controller Parameters via Multi-Fidelity Bayesian Optimization: From Simulation to Experiment—0
Robust Evolutionary Multi-Objective Network Architecture Search for Reinforcement Learning (EMNAS-RL)—0
Do MIL Models Transfer?Code2
SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging SegmentationCode0
Data-Efficient Challenges in Visual Inductive Priors: A Retrospective—0
Variational Supervised Contrastive Learning—0
The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine LearningCode0
CrosswalkNet: An Optimized Deep Learning Framework for Pedestrian Crosswalk Detection in Aerial Images with High-Performance Computing—0
ETT-CKGE: Efficient Task-driven Tokens for Continual Knowledge Graph EmbeddingCode0
Flowing Datasets with Wasserstein over Wasserstein Gradient FlowsCode1
Transfer Learning and Explainable AI for Brain Tumor Classification: A Study Using MRI Data from Bangladesh—0
Exploring Image Transforms derived from Eye Gaze Variables for Progressive Autism Diagnosis—0
The OCR Quest for Generalization: Learning to recognize low-resource alphabets with model editing—0
Textile Analysis for Recycling Automation using Transfer Learning and Zero-Shot Foundation Models—0
When Models Know More Than They Can Explain: Quantifying Knowledge Transfer in Human-AI Collaboration—0
DiCoRe: Enhancing Zero-shot Event Detection via Divergent-Convergent LLM Reasoning—0
OpenAg: Democratizing Agricultural Intelligence—0
Generating Synthetic Stereo Datasets using 3D Gaussian Splatting and Expert Knowledge Transfer—0
LESS: Large Language Model Enhanced Semi-Supervised Learning for Speech Foundational Models—0
Self-Composing Policies for Scalable Continual Reinforcement Learning—0
Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to 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