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 1601–1625 of 10307 papers

TitleStatusHype
Beamforming and Resource Allocation for Delay Optimization in RIS-Assisted OFDM Systems—0
Self-Composing Policies for Scalable Continual Reinforcement Learning—0
Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning—0
StARS DCM: A Sleep Stage-Decoding Forehead EEG Patch for Real-time Modulation of Sleep Physiology—0
Multi-Platform Methane Plume Detection via Model and Domain Adaptation—0
TaxaDiffusion: Progressively Trained Diffusion Model for Fine-Grained Species GenerationCode0
Getting More from Less: Transfer Learning Improves Sleep Stage Decoding Accuracy in Peripheral Wearable Devices—0
COGNATE: Acceleration of Sparse Tensor Programs on Emerging Hardware using Transfer Learning—0
Progressive Class-level Distillation—0
Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image SegmentationCode0
Proactive Guidance of Multi-Turn Conversation in Industrial Search—0
LLMs Are Globally Multilingual Yet Locally Monolingual: Exploring Knowledge Transfer via Language and Thought Theory—0
Attractor learning for spatiotemporally chaotic dynamical systems using echo state networks with transfer learning—0
Improving Language and Modality Transfer in Translation by Character-level Modeling—0
Lightweight Convolutional Neural Networks for Retinal Disease Classification—0
Epistemic Errors of Imperfect Multitask Learners When Distributions Shift—0
BIRD: Behavior Induction via Representation-structure Distillation—0
Personalized Subgraph Federated Learning with Differentiable Auxiliary Projections—0
Graph Positional Autoencoders as Self-supervised Learners—0
Knowledge Insulating Vision-Language-Action Models: Train Fast, Run Fast, Generalize Better—0
Zero-Shot Adaptation of Parameter-Efficient Fine-Tuning in Diffusion Models—0
BugWhisperer: Fine-Tuning LLMs for SoC Hardware Vulnerability Detection—0
GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data—0
Chest Disease Detection In X-Ray Images Using Deep Learning Classification Method—0
InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective—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