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 3451–3475 of 10307 papers

TitleStatusHype
Enhancing Knowledge Distillation for LLMs with Response-Priming PromptingCode0
Enhancing Human Pose Estimation in Ancient Vase Paintings via Perceptually-grounded Style Transfer LearningCode0
Enhancing Scene Classification in Cloudy Image Scenarios: A Collaborative Transfer Method with Information Regulation Mechanism using Optical Cloud-Covered and SAR Remote Sensing ImagesCode0
Enhancing Dataset Distillation via Non-Critical Region RefinementCode0
Enhancing Drug-Target Interaction Prediction through Transfer Learning from Activity Cliff Prediction TasksCode0
Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer LearningCode0
Constrained Decoding for Cross-lingual Label ProjectionCode0
Enhancing Cross-Dataset Performance of Distracted Driving Detection With Score Softmax Classifier And Dynamic Gaussian Smoothing SupervisionCode0
Enhancing Generalized Few-Shot Semantic Segmentation via Effective Knowledge TransferCode0
End-to-End Video Question-Answer Generation with Generator-Pretester NetworkCode0
Fonts-2-Handwriting: A Seed-Augment-Train framework for universal digit classificationCode0
Towards Training Music Taggers on Synthetic DataCode0
Encodings for Prediction-based Neural Architecture SearchCode0
A Neural Network based Framework for Effective Laparoscopic Video Quality AssessmentCode0
Consensus Focus for Object Detection and minority classesCode0
End-to-End Deep Learning of Optimization HeuristicsCode0
Enhancing Brain Tumor Segmentation Using Channel Attention and Transfer learningCode0
Enhancing textual textbook question answering with large language models and retrieval augmented generationCode0
Challenging the Assumption of Structure-based embeddings in Few- and Zero-shot Knowledge Graph CompletionCode0
Estimating Buildings' Parameters over Time Including Prior KnowledgeCode0
RASNet: Segmentation for Tracking Surgical Instruments in Surgical Videos Using Refined Attention Segmentation NetworkCode0
Connecting the Dots between Audio and Text without Parallel Data through Visual Knowledge Transfer—0
Assessing Large Language Models for Online Extremism Research: Identification, Explanation, and New Knowledge—0
Conjuring Positive Pairs for Efficient Unification of Representation Learning and Image Synthesis—0
Assessing and Learning Alignment of Unimodal Vision and Language Models—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