Linear-Probe Classification
7 papers with code • 2 benchmarks • 2 datasets
Libraries
Use these libraries to find Linear-Probe Classification models and implementations
ImageNet
SentEval
Most implemented papers
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
google-research/bert
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NAACL 2019
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
UKPLab/sentence-transformers
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IJCNLP 2019
However, it requires that both sentences are fed into the network, which causes a massive computational overhead: Finding the most similar pair in a collection of 10, 000 sentences requires about 50 million inference computations (~65 hours) with BERT.
SimCSE: Simple Contrastive Learning of Sentence Embeddings
princeton-nlp/SimCSE
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EMNLP 2021
This paper presents SimCSE, a simple contrastive learning framework that greatly advances state-of-the-art sentence embeddings.
DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations
JohnGiorgi/DeCLUTR
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ACL 2021
Inspired by recent advances in deep metric learning (DML), we carefully design a self-supervised objective for learning universal sentence embeddings that does not require labelled training data.
Text and Code Embeddings by Contrastive Pre-Training
Similarly to text embeddings, we train code embedding models on (text, code) pairs, obtaining a 20. 8% relative improvement over prior best work on code search.
Neural Eigenfunctions Are Structured Representation Learners
In this paper, we introduce a scalable method for learning structured, adaptive-length deep representations.
DINO-MC: Self-supervised Contrastive Learning for Remote Sensing Imagery with Multi-sized Local Crops
wennyxy/dino-mc
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Due to the costly nature of remote sensing image labeling and the large volume of available unlabeled imagery, self-supervised methods that can learn feature representations without manual annotation have received great attention.
