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 6551–6600 of 10307 papers

TitleStatusHype
In Rain or Shine: Understanding and Overcoming Dataset Bias for Improving Robustness Against Weather Corruptions for Autonomous Vehicles—0
InsertRank: LLMs can reason over BM25 scores to Improve Listwise Reranking—0
In-Situ Melt Pool Characterization via Thermal Imaging for Defect Detection in Directed Energy Deposition Using Vision Transformers—0
Inspect Transfer Learning Architecture with Dilated Convolution—0
Inspiration Learning through Preferences—0
Instance-based Deep Transfer Learning—0
Instance-based Inductive Deep Transfer Learning by Cross-Dataset Querying with Locality Sensitive Hashing—0
Instance-based Transfer Learning for Multilingual Deep Retrieval—0
Instance-weighted Transfer Learning of Active Appearance Models—0
Attend and Enrich: Enhanced Visual Prompt for Zero-Shot Learning—0
Instruction-Guided Autoregressive Neural Network Parameter Generation—0
Integrating Deep Features for Material Recognition—0
Integration of Convolutional Neural Networks for Pulmonary Nodule Malignancy Assessment in a Lung Cancer Classification Pipeline—0
Intelligent Chemical Purification Technique Based on Machine Learning—0
Intelligent Incident Hypertension Prediction in Obstructive Sleep Apnea—0
Intelligent multicast routing method based on multi-agent deep reinforcement learning in SDWN—0
Intelligent multiscale simulation based on process-guided composite database—0
Interaction-Aware Personalized Vehicle Trajectory Prediction Using Temporal Graph Neural Networks—0
Interactive dimensionality reduction using similarity projections—0
Interactive DualChecker for Mitigating Hallucinations in Distilling Large Language Models—0
InteRACT: Transformer Models for Human Intent Prediction Conditioned on Robot Actions—0
Inter- and Intra-domain Knowledge Transfer for Related Tasks in Deep Character Recognition—0
Inter-Cell Network Slicing With Transfer Learning Empowered Multi-Agent Deep Reinforcement Learning—0
Intermediate-Task Transfer Learning: Leveraging Sarcasm Detection for Stance Detection—0
Intermediate-Task Transfer Learning with Pretrained Models for Natural Language Understanding: When and Why Does It Work?—0
Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work?—0
Interpolation-Free Deep Learning for Meteorological Downscaling on Unaligned Grids Across Multiple Domains with Application to Wind Power—0
Interpretable and synergistic deep learning for visual explanation and statistical estimations of segmentation of disease features from medical images—0
Interpretable Deep Learning applied to Plant Stress Phenotyping—0
Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction—0
Interpretation of Chest x-rays affected by bullets using deep transfer learning—0
Redundancy and Concept Analysis for Code-trained Language Models—0
Inter-subject Deep Transfer Learning for Motor Imagery EEG Decoding—0
Inter-Subject Variance Transfer Learning for EMG Pattern Classification Based on Bayesian Inference—0
Intra-domain and cross-domain transfer learning for time series data -- How transferable are the features?—0
Intra-Domain Task-Adaptive Transfer Learning to Determine Acute Ischemic Stroke Onset Time—0
Intra-Inter Subject Self-supervised Learning for Multivariate Cardiac Signals—0
Robustness via Deep Low-Rank Representations—0
Intrinsic Geometric Information Transfer Learning on Multiple Graph-Structured Datasets—0
Introducing the structural bases of typicality effects in deep learning—0
Introspective Action Advising for Interpretable Transfer Learning—0
Your representations are in the network: composable and parallel adaptation for large scale models—0
Inverse Density as an Inverse Problem: The Fredholm Equation Approach—0
Inverse Design of Grating Couplers Using the Policy Gradient Method from Reinforcement Learning—0
Inverse design optimization framework via a two-step deep learning approach: application to a wind turbine airfoil—0
Inverse design with conditional cascaded diffusion models—0
Inverse Reinforcement Learning with Simultaneous Estimation of Rewards and Dynamics—0
Investigating and Exploiting Image Resolution for Transfer Learning-based Skin Lesion Classification—0
Investigating Continual Pretraining in Large Language Models: Insights and Implications—0
Investigating Continuous Learning in Spiking Neural Networks—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