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 5051–5100 of 10307 papers

TitleStatusHype
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
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
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
Progressive reduced order modeling: empowering data-driven modeling with selective knowledge transfer—0
Progressive Reinforcement Learning with Distillation for Multi-Skilled Motion Control—0
Progressive trajectory matching for medical dataset distillation—0
Progressive transfer learning for low frequency data prediction in full waveform inversion—0
Progressive Transfer Learning for Multi-Pass Fundus Image Restoration—0
Project and Probe: Sample-Efficient Domain Adaptation by Interpolating Orthogonal Features—0
Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research—0
Prompted Meta-Learning for Few-shot Knowledge Graph Completion—0
Prompting Large Language Model for Machine Translation: A Case Study—0
Propagation-aware Social Recommendation by Transfer Learning—0
Proper Reuse of Image Classification Features Improves Object Detection—0
ProQA: Structural Prompt-based Pre-training for Unified Question Answering—0
ProRuka: A highly efficient HMI algorithm for controlling a novel prosthetic hand with 6-DOF using sonomyography—0
ProtoDA: Efficient Transfer Learning for Few-Shot Intent Classification—0
ProtoTransfer: Cross-Modal Prototype Transfer for Point Cloud Segmentation—0
Prototype-Guided Memory Replay for Continual Learning—0
Prototypical Contrastive Transfer Learning for Multimodal Language Understanding—0
Prototypical Cross-domain Knowledge Transfer for Cervical Dysplasia Visual Inspection—0
Prototypical Metric Transfer Learning for Continuous Speech Keyword Spotting With Limited Training Data—0
Prototypical Model with Novel Information-theoretic Loss Function for Generalized Zero Shot Learning—0
Provable benefits of representation learning—0
Provable Benefits of Unsupervised Pre-training and Transfer Learning via Single-Index Models—0
Provable Sample-Efficient Transfer Learning Conditional Diffusion Models via Representation Learning—0
Transfer Learning with Partially Observable Offline Data via Causal Bounds—0
Provably Robust Transfer—0
Proxy-informed Bayesian transfer learning with unknown sources—0
Prune Once for All: Sparse Pre-Trained Language Models—0
Pruning Adatperfusion with Lottery Ticket Hypothesis—0
Pruning Adatperfusion with Lottery Ticket Hypothesis—0
PSDNet: Determination of Particle Size Distributions Using Synthetic Soil Images and Convolutional Neural Networks—0
Regularizing CNN Transfer Learning with Randomised Regression—0
P-Swish: Activation Function with Learnable Parameters Based on Swish Activation Function in Deep Learning—0
Psychological Health Knowledge-Enhanced LLM-based Social Network Crisis Intervention Text Transfer Recognition Method—0
Psychological State in Text: A Limitation of Sentiment Analysis—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