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 26–50 of 10307 papers

TitleStatusHype
Benchmarking histopathology foundation models in a multi-center dataset for skin cancer subtypingCode0
Generalizing Vision-Language Models to Novel Domains: A Comprehensive Survey—0
Leveraging neural network interatomic potentials for a foundation model of chemistry—0
These are Not All the Features You are Looking For: A Fundamental Bottleneck In Supervised PretrainingCode0
Rethinking the Role of Operating Conditions for Learning-based Multi-condition Fault Diagnosis—0
Numerical simulation of transient heat conduction with moving heat source using Physics Informed Neural Networks—0
Pieceformer: Similarity-Driven Knowledge Transfer via Scalable Graph Transformer in VLSI—0
Co-Creative Learning via Metropolis-Hastings Interaction between Humans and AI—0
Leveraging Transfer Learning and User-Specific Updates for Rapid Training of BCI Decoders—0
Bayesian Knowledge Transfer for a Kalman Fixed-Lag Interval Smoother—0
AgentDistill: Training-Free Agent Distillation with Generalizable MCP Boxes—0
Adjustment for Confounding using Pre-Trained RepresentationsCode0
Less is More: Undertraining Experts Improves Model Upcycling—0
DiFuse-Net: RGB and Dual-Pixel Depth Estimation using Window Bi-directional Parallax Attention and Cross-modal Transfer Learning—0
InsertRank: LLMs can reason over BM25 scores to Improve Listwise Reranking—0
Evolution of ReID: From Early Methods to LLM Integration—0
Understand the Implication: Learning to Think for Pragmatic Understanding—0
A Transfer Learning Framework for Multilayer Networks via Model Averaging—0
Cross-Domain Conditional Diffusion Models for Time Series ImputationCode0
Coefficient Shape Transfer Learning for Functional Linear Regression—0
BotTrans: A Multi-Source Graph Domain Adaptation Approach for Social Bot DetectionCode0
Uncertainty-Aware Deep Learning for Automated Skin Cancer Classification: A Comprehensive Evaluation—0
Auto-Compressing Networks—0
Attention on flow control: transformer-based reinforcement learning for lift regulation in highly disturbed flows—0
An Effective End-to-End Solution for Multimodal Action Recognition—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