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 3401–3450 of 10307 papers

TitleStatusHype
Exploration of Adapter for Noise Robust Automatic Speech Recognition—0
Deep Neural Network Models Trained With A Fixed Random Classifier Transfer Better Across Domains—0
Automated Testing of Spatially-Dependent Environmental Hypotheses through Active Transfer Learning—0
Investigating Continual Pretraining in Large Language Models: Insights and Implications—0
Beyond the Known: Investigating LLMs Performance on Out-of-Domain Intent Detection—0
Transfer Learning Bayesian Optimization to Design Competitor DNA Molecules for Use in Diagnostic AssaysCode0
Intensive Care as One Big Sequence Modeling ProblemCode0
Enhancing Continuous Domain Adaptation with Multi-Path Transfer Curriculum—0
DTCM: Deep Transformer Capsule Mutual Distillation for Multivariate Time Series Classification—0
GenAINet: Enabling Wireless Collective Intelligence via Knowledge Transfer and Reasoning—0
Few-Shot Learning for Annotation-Efficient Nucleus Instance Segmentation—0
Adversarial-Robust Transfer Learning for Medical Imaging via Domain Assimilation—0
Exploring the Power of Pure Attention Mechanisms in Blind Room Parameter Estimation—0
StochCA: A Novel Approach for Exploiting Pretrained Models with Cross-AttentionCode0
Task Specific Pretraining with Noisy Labels for Remote Sensing Image Segmentation—0
Emotion Classification in Short English Texts using Deep Learning Techniques—0
MMW-Carry: Enhancing Carry Object Detection through Millimeter-Wave Radar-Camera Fusion—0
Artificial Bee Colony optimization of Deep Convolutional Neural Networks in the context of Biomedical Imaging—0
Substrate Prediction for RiPP Biosynthetic Enzymes via Masked Language Modeling and Transfer LearningCode0
PEMT: Multi-Task Correlation Guided Mixture-of-Experts Enables Parameter-Efficient Transfer Learning—0
Which Model to Transfer? A Survey on Transferability Estimation—0
Smoothness Adaptive Hypothesis Transfer Learning—0
CLCE: An Approach to Refining Cross-Entropy and Contrastive Learning for Optimized Learning Fusion—0
SHM-Traffic: DRL and Transfer learning based UAV Control for Structural Health Monitoring of Bridges with Traffic—0
TIE-KD: Teacher-Independent and Explainable Knowledge Distillation for Monocular Depth EstimationCode0
Global Safe Sequential Learning via Efficient Knowledge TransferCode0
Practical Insights into Knowledge Distillation for Pre-Trained Models—0
ARL2: Aligning Retrievers for Black-box Large Language Models via Self-guided Adaptive Relevance LabelingCode0
Wisdom of Committee: Distilling from Foundation Model to Specialized Application Model—0
Simple and Effective Transfer Learning for Neuro-Symbolic Integration—0
Indiscriminate Data Poisoning Attacks on Pre-trained Feature Extractors—0
Scalable and reliable deep transfer learning for intelligent fault detection via multi-scale neural processes embedded with knowledge—0
LinkSAGE: Optimizing Job Matching Using Graph Neural Networks—0
Learning Causal Domain-Invariant Temporal Dynamics for Few-Shot Action Recognition—0
CST: Calibration Side-Tuning for Parameter and Memory Efficient Transfer Learning—0
Cross-Domain Transfer Learning with CoRTe: Consistent and Reliable Transfer from Black-Box to Lightweight Segmentation Model—0
Molecule Generation and Optimization for Efficient Fragrance CreationCode0
Key ingredients for effective zero-shot cross-lingual knowledge transfer in generative tasks—0
Predicting trucking accidents with truck drivers 'safety climate perception across companies: A transfer learning approach—0
Enhancing Multilingual Capabilities of Large Language Models through Self-Distillation from Resource-Rich Languages—0
Induced Model Matching: How Restricted Models Can Help Larger OnesCode0
Stealing the Invisible: Unveiling Pre-Trained CNN Models through Adversarial Examples and Timing Side-Channels—0
A synthetic data approach for domain generalization of NLI models—0
Mitigating Catastrophic Forgetting in Multi-domain Chinese Spelling Correction by Multi-stage Knowledge Transfer Framework—0
Autocorrect for Estonian texts: final report from project EKTB25—0
A Question Answering Based Pipeline for Comprehensive Chinese EHR Information Extraction—0
Differential Private Federated Transfer Learning for Mental Health Monitoring in Everyday Settings: A Case Study on Stress Detection—0
Personalised Drug Identifier for Cancer Treatment with Transformers using Auxiliary InformationCode0
Robust agents learn causal world models—0
Towards Precision Cardiovascular Analysis in Zebrafish: The ZACAF Paradigm—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