Methods › General › Self-Supervised Learning › SEER

SEER

19 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

SEER is a self-supervised learning approach for training large models on random, uncurated images with no supervision. It trains RegNet-Y architectures with the SwAV. Several adjustments are made to self-supervised training to make it work at a larger scale, including using a cosine learning schedule

Source: Self-supervised Pretraining of Visual Features in the Wild

Papers archive 2025-07-28

19 shown of 19, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 38 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Survival Analysis5
Prognosis4
Reinforcement Learning3
Transfer Learning3
reinforcement-learning3
Epidemiology2
Federated Learning2
Question Answering2
Reinforcement Learning (RL)2
Atari Games1
BIG-bench Machine Learning1
Chunking1
Computational Efficiency1
Decoder1
Deep Reinforcement Learning1
Diversity1
Feature Importance1
High-Level Synthesis1
Image Classification1
Imputation1

Usage over time archive 2025-07-28

Papers per year tagged with SEER: 2021 to 2025, peak 5 5 0 2021: 5 papers 2021 2022: 2 papers 2022 2023: 4 papers 2023 2024: 4 papers 2024 2025: 4 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (19 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Self-Supervised Learning

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