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 1151–1175 of 10307 papers

TitleStatusHype
How Effective is Pre-training of Large Masked Autoencoders for Downstream Earth Observation Tasks?—0
Jump Diffusion-Informed Neural Networks with Transfer Learning for Accurate American Option Pricing under Data Scarcity—0
Automated Segmentation and Analysis of Microscopy Images of Laser Powder Bed Fusion Melt Tracks—0
Transfer Learning in _1 Regularized Regression: Hyperparameter Selection Strategy based on Sharp Asymptotic Analysis—0
MaskLLM: Learnable Semi-Structured Sparsity for Large Language ModelsCode2
T3: A Novel Zero-shot Transfer Learning Framework Iteratively Training on an Assistant Task for a Target Task—0
Speech Recognition Rescoring with Large Speech-Text Foundation Models—0
GraphLoRA: Structure-Aware Contrastive Low-Rank Adaptation for Cross-Graph Transfer LearningCode1
Cross-Lingual Speech Emotion Recognition: Humans vs. Self-Supervised ModelsCode0
Graph Pruning Based Spatial and Temporal Graph Convolutional Network with Transfer Learning for Traffic Prediction—0
Transfer learning for financial data predictions: a systematic review—0
Lessons and Insights from a Unifying Study of Parameter-Efficient Fine-Tuning (PEFT) in Visual RecognitionCode1
Unleashing the Potential of Synthetic Images: A Study on Histopathology Image ClassificationCode0
Personalized Federated Learning via Backbone Self-Distillation—0
Machine Translation Advancements of Low-Resource Indian Languages by Transfer Learning—0
Online Multi-level Contrastive Representation Distillation for Cross-Subject fNIRS Emotion RecognitionCode0
SeqNet: Sequential Networks for One-Shot Traffic Sign Recognition With Transfer LearningCode0
Micrometer: Micromechanics Transformer for Predicting Mechanical Responses of Heterogeneous Materials—0
CON: Continual Object Navigation via Data-Free Inter-Agent Knowledge Transfer in Unseen and Unfamiliar Places—0
DSG-KD: Knowledge Distillation from Domain-Specific to General Language ModelsCode0
From Lazy to Rich: Exact Learning Dynamics in Deep Linear Networks—0
Generalization in birdsong classification: impact of transfer learning methods and dataset characteristics—0
Multiple-Exit Tuning: Towards Inference-Efficient Adaptation for Vision Transformer—0
Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Tensorflow Pretrained Models—0
Context-Aware Predictive Coding: A Representation Learning Framework for WiFi SensingCode0
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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