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 8851–8875 of 10307 papers

TitleStatusHype
Probabilistic Multi-Layer Perceptrons for Wind Farm Condition Monitoring—0
Probabilistic Neural Network with Complex Exponential Activation Functions in Image Recognition using Deep Learning Framework—0
The Information Complexity of Learning Tasks, their Structure and their Distance—0
Probabilistic Reasoning via Deep Learning: Neural Association Models—0
Probabilistic Self-supervised Learning via Scoring Rules Minimization—0
Probabilistic transfer learning methodology to expedite high fidelity simulation of reactive flows—0
Probing Out-of-Distribution Robustness of Language Models with Parameter-Efficient Transfer Learning—0
Probing Transfer in Deep Reinforcement Learning without Task Engineering—0
Probing transfer learning with a model of synthetic correlated datasets—0
Probing TryOnGAN—0
Problems in AI research and how the SP System may help to solve them—0
Procedural Content Generation via Knowledge Transformation (PCG-KT)—0
ProductNet: a Collection of High-Quality Datasets for Product Representation Learning—0
SURFNet: Super-resolution of Turbulent Flows with Transfer Learning using Small Datasets—0
Programmable metasurfaces for future photonic artificial intelligence—0
Programmable Neural Network Trojan for Pre-Trained Feature Extractor—0
Program-to-Circuit: Exploiting GNNs for Program Representation and Circuit Translation—0
Progressive Class-level Distillation—0
6th Place Solution to Google Universal Image Embedding—0
Progressive Joint Modeling in Unsupervised Single-channel Overlapped Speech Recognition—0
Progressive Knowledge Transfer Based on Human Visual Perception Mechanism for Perceptual Quality Assessment of Point Clouds—0
Addressing Asymmetry in Multilingual Neural Machine Translation with Fuzzy Task Clustering—0
Surgical Phase Recognition of Short Video Shots Based on Temporal Modeling of Deep Features—0
Progressive reduced order modeling: empowering data-driven modeling with selective knowledge transfer—0
Progressive Reinforcement Learning with Distillation for Multi-Skilled Motion Control—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