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 4851–4875 of 10307 papers

TitleStatusHype
Industrial Scene Change Detection using Deep Convolutional Neural Networks—0
Inexact-ADMM Based Federated Meta-Learning for Fast and Continual Edge Learning—0
Statistical Inference for Maximin Effects: Identifying Stable Associations across Multiple Studies—0
Inference is All You Need: Self Example Retriever for Cross-domain Dialogue State Tracking with ChatGPT—0
Better transfer learning with inferred successor maps—0
Inferring Analogous Attributes—0
InfLocNet: Enhanced Lung Infection Localization and Disease Detection from Chest X-Ray Images Using Lightweight Deep Learning—0
Info3D: Representation Learning on 3D Objects using Mutual Information Maximization and Contrastive Learning—0
InFoBERT: Zero-Shot Approach to Natural Language Understanding Using Contextualized Word Embedding—0
Information-theoretic analysis for transfer learning—0
Information-Theoretic Bounds on Transfer Generalization Gap Based on Jensen-Shannon Divergence—0
Informative and Consistent Correspondence Mining for Cross-Domain Weakly Supervised Object Detection—0
Informed Forecasting: Leveraging Auxiliary Knowledge to Boost LLM Performance on Time Series Forecasting—0
InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective—0
Injecting Prior Knowledge for Transfer Learning into Reinforcement Learning Algorithms using Logic Tensor Networks—0
In Neural Machine Translation, What Does Transfer Learning Transfer?—0
Innovative Speech-Based Deep Learning Approaches for Parkinson's Disease Classification: A Systematic Review—0
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
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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