Semi Supervised Image Classification On 1
評価指標
Top 1 Accuracy
評価結果
このベンチマークにおける各モデルのパフォーマンス結果
比較表
| モデル名 | Top 1 Accuracy |
|---|---|
| semi-supervised-learning-of-visual-features | 66.5% |
| 190503670 | - |
| big-self-supervised-models-are-strong-semi | 74.9% |
| vicreg-variance-invariance-covariance | 54.8% |
| 190503670 | - |
| vision-models-are-more-robust-and-fair-when | 62.4% |
| big-self-supervised-models-are-strong-semi | 75.9% |
| semi-supervised-vision-transformers-at-scale | 71% |
| barlow-twins-self-supervised-learning-via | 55% |
| debiased-learning-from-naturally-imbalanced | 71.3% |
| simmatchv2-semi-supervised-learning-with | 71.9% |
| semi-supervised-learning-of-visual-features | 69.6% |
| a-simple-framework-for-contrastive-learning | 63.0% |
| bootstrap-your-own-latent-a-new-approach-to | 53.2% |
| representation-learning-with-contrastive | - |
| big-self-supervised-models-are-strong-semi | 73.9% |
| a-simple-framework-for-contrastive-learning | 58.5% |
| debiasing-calibrating-and-improving-semi | 67.4% |
| 190503670 | - |
| comatch-semi-supervised-learning-with | 67.1% |
| with-a-little-help-from-my-friends-nearest | 56.4% |
| unsupervised-learning-of-visual-features-by | 53.9% |
| semi-supervised-vision-transformers-at-scale | 80% |
| semireward-a-general-reward-model-for-semi | 59.64% |
| モデル 25 | 52.7% |
| bootstrap-your-own-latent-a-new-approach-to | 62.2% |
| exponential-moving-average-normalization-for | 63% |
| 190503670 | - |
| semi-supervised-learning-of-visual-features | 69.9% |
| prototypical-contrastive-learning-of | - |
| big-self-supervised-models-are-strong-semi | 76.6% |
| vne-an-effective-method-for-improving-deep | 55.8 |
| online-bag-of-visual-words-generation-for | - |
| weakly-supervised-contrastive-learning-1 | 65.0% |
| a-simple-framework-for-contrastive-learning | 48.3% |
| large-scale-adversarial-representation | - |
| semi-supervised-vision-transformers-at-scale | 77.3% |
| big-self-supervised-models-are-strong-semi | 57.9% |
| bootstrap-your-own-latent-a-new-approach-to | 71.2% |
| boosting-contrastive-self-supervised-learning | 63.7% |
| bootstrap-your-own-latent-a-new-approach-to | 69.1% |
| self-supervised-pretraining-of-visual | 60.5% |
| ibot-image-bert-pre-training-with-online | 61.9% |
| self-supervised-learning-by-estimating-twin-1 | 67.2% |
| masked-siamese-networks-for-label-efficient | 75.7% |
| 190503670 | - |
| big-self-supervised-models-are-strong-semi | 66.3% |
| learning-to-classify-images-without-labels | 39.90% |
| pushing-the-limits-of-self-supervised-resnets | 58.1% |
| debiasing-calibrating-and-improving-semi | 68.6% |
| meta-co-training-two-views-are-better-than | 80.7% |
| unsupervised-feature-learning-via-non-1 | - |
| learning-customized-visual-models-with | 81.6% |
| synco-synthetic-hard-negatives-in-contrastive | 50.8% |
| 190503670 | - |
| self-supervised-pretraining-of-visual | 57.5% |
| 190503670 | - |
| simmatch-semi-supervised-learning-with | 67.2% |