Linear-Probe Classification

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

2 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

11 shown of 11 papers with code (11 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
  11 Oct 2018 534 repositories listed Syntology ran 204 of 659 samples · 455 unverified · 149 pointer-only (licence)
  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
  27 Aug 2019 64 repositories listed Syntology ran 20 of 58 samples · 38 unverified · 11 pointer-only (licence)
  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…
• SimCSE: Simple Contrastive Learning of Sentence Embeddings
  18 Apr 2021 23 repositories listed Syntology ran 17 of 30 samples · 13 unverified · 19 pointer-only (licence)
  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
  5 Jun 2020 2 repositories listed Syntology ran 0 of 1 samples · 1 unverified
  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.
• Latent Diffusion Autoencoders: Toward Efficient and Meaningful Unsupervised Representation Learning in Medical Imaging
  11 Apr 2025 1 repository listed
  This study presents Latent Diffusion Autoencoder (LDAE), a novel encoder-decoder diffusion-based framework for efficient and meaningful unsupervised learning in medical imaging, focusing on Alzheimer disease (AD) using…
• Stabilize the Latent Space for Image Autoregressive Modeling: A Unified Perspective
  16 Oct 2024 1 repository listed Syntology ran 3 of 5 samples · 2 unverified
  Furthermore, we propose a simple but effective discrete image tokenizer to stabilize the latent space for image generative modeling by applying K-Means on the latent features of self-supervised learning models.
• SODA: Bottleneck Diffusion Models for Representation Learning
  29 Nov 2023 1 repository listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  We introduce SODA, a self-supervised diffusion model, designed for representation learning.
• Extending global-local view alignment for self-supervised learning with remote sensing imagery
  12 Mar 2023 1 repository listed Syntology ran 0 of 3 samples · 3 unverified
  While prior studies have explored multiple self-supervised learning techniques in remote sensing domain, pretext tasks based on local-global view alignment remain underexplored, despite achieving state-of-the-art…
• Scaling Vision Transformers to 22 Billion Parameters
  10 Feb 2023 1 repository listed
  The scaling of Transformers has driven breakthrough capabilities for language models.
• Neural Eigenfunctions Are Structured Representation Learners
  23 Oct 2022 1 repository listed
  Unlike prior spectral methods such as Laplacian Eigenmap that operate in a nonparametric manner, Neural Eigenmap leverages NeuralEF to parametrically model eigenfunctions using a neural network.
• Text and Code Embeddings by Contrastive Pre-Training
  24 Jan 2022 1 repository listed
  Similarly to text embeddings, we train code embedding models on (text, code) pairs, obtaining a 20.

Syntology lines on 7 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.