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 4926–4950 of 10307 papers

TitleStatusHype
Pieceformer: Similarity-Driven Knowledge Transfer via Scalable Graph Transformer in VLSI—0
PIMKL: Pathway Induced Multiple Kernel Learning—0
PINGAN Omini-Sinitic at SemEval-2022 Task 4: Multi-prompt Training for Patronizing and Condescending Language Detection—0
PIRC Net : Using Proposal Indexing, Relationships and Context for Phrase Grounding—0
Pitch Contour Exploration Across Audio Domains: A Vision-Based Transfer Learning Approach—0
Pit-Pattern Classification of Colorectal Cancer Polyps Using a Hyper Sensitive Vision-Based Tactile Sensor and Dilated Residual Networks—0
Pivotal Role of Language Modeling in Recommender Systems: Enriching Task-specific and Task-agnostic Representation Learning—0
Pivot-based Transfer Learning for Neural Machine Translation between Non-English Languages—0
Pivot Based Transfer Learning for Neural Machine Translation: CFILT IITB @ WMT 2021 Triangular MT—0
Pixel to policy: DQN Encoders for within & cross-game reinforcement learning—0
Plansformer: Generating Symbolic Plans using Transformers—0
Plant Disease Detection from Images—0
Plant Species Classification Using Transfer Learning by Pretrained Classifier VGG-19—0
Plaque Classification in Coronary Arteries from IVOCT Images Using Convolutional Neural Networks and Transfer Learning—0
Plug and Play, Model-Based Reinforcement Learning—0
Pluvio: Assembly Clone Search for Out-of-domain Architectures and Libraries through Transfer Learning and Conditional Variational Information Bottleneck—0
P-MMEval: A Parallel Multilingual Multitask Benchmark for Consistent Evaluation of LLMs—0
PMT-MAE: Dual-Branch Self-Supervised Learning with Distillation for Efficient Point Cloud Classification—0
PMU measurements based short-term voltage stability assessment of power systems via deep transfer learning—0
Pneumonia Detection in Chest X-Ray Images : Handling Class Imbalance—0
Pneumonia Detection in Chest X-Rays using Neural Networks—0
PNN: A Novel Progressive Neural Network for Fault Classification in Rotating Machinery under Small Dataset Constraint—0
PoE: a Panel of Experts for Generalized Automatic Dialogue Assessment—0
Point Clouds Are Specialized Images: A Knowledge Transfer Approach for 3D Understanding—0
Point Cloud Segmentation Using Transfer Learning with RandLA-Net: A Case Study on Urban Areas—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