Image Classification On Stl 10
评估指标
Percentage correct
评测结果
各个模型在此基准测试上的表现结果
比较表格
| 模型名称 | Percentage correct |
|---|---|
| training-neural-networks-with-local-error | 80.75 |
| mixmatch-a-holistic-approach-to-semi | 88.80 |
| image-augmentation-for-object-image | 84.38 |
| neural-architecture-transfer | 97.8 |
| a-framework-for-contrastive-self-supervised | 93.80 |
| increasing-trustworthiness-of-deep-neural | 93.19 |
| fixmatch-simplifying-semi-supervised-learning-1 | 94.77 |
| a-framework-for-contrastive-self-supervised | 61 |
| an-analysis-of-unsupervised-pre-training-in | 70.2 |
| a-continual-development-methodology-for-large | 99.64 |
| toward-understanding-supervised | 98.34 |
| scaling-the-scattering-transform-deep-hybrid | 75.7 |
| fixmatch-simplifying-semi-supervised-learning-1 | 94.83 |
| fixmatch-simplifying-semi-supervised-learning-1 | 92.02 |
| neural-architecture-transfer | 96.7 |
| increasing-trustworthiness-of-deep-neural | 68.62 |
| scaling-the-scattering-transform-deep-hybrid | 74.33 |
| reversible-architectures-for-arbitrarily-deep | 83.7 |
| general-e2-equivariant-steerable-cnns-1 | 88.95 |
| extended-batch-normalization | 81.04 |
| semi-supervised-learning-with-context | 77.8 |
| generative-pretraining-from-pixels | 97.1 |
| effective-version-space-reduction-for | 57.35 |
| remixmatch-semi-supervised-learning-with-1 | 93.23 |
| general-e2-equivariant-steerable-cnns-1 | 89.43 |
| no-more-meta-parameter-tuning-in-unsupervised | 61 |
| hybridnet-classification-and-reconstruction | 84.10 |
| fixmatch-simplifying-semi-supervised-learning-1 | 92.34 |
| 190600910 | 94.5 |
| neural-architecture-transfer | 97.9 |
| deep-neural-networks-motivated-by-partial | 74.3 |
| multi-task-bayesian-optimization | 70.1 |
| reversible-architectures-for-arbitrarily-deep | 85.5 |
| committees-of-deep-feedforward-networks | 68 |
| effective-version-space-reduction-for | 59.45 |
| scaling-the-scattering-transform-deep-hybrid | 70.7 |
| increasing-trustworthiness-of-deep-neural | 71.05 |
| toward-understanding-supervised | 98.36 |
| toward-understanding-supervised | 98.45 |
| toward-understanding-supervised | 98.17 |
| mixmatch-a-holistic-approach-to-semi | 87.36 |
| convolutional-kernel-networks | 62.3 |
| invariant-information-distillation-for | 88.8 |
| probabilistic-structural-latent | 83.2 |
| effective-version-space-reduction-for | 58.81 |
| effective-version-space-reduction-for | 58.15 |
| greedy-infomax-for-biologically-plausible | 81.9 |
| deep-neural-networks-motivated-by-partial | 77.0 |
| extended-batch-normalization | 76.49 |
| harmonic-networks-with-limited-training | 90.45 |
| effective-version-space-reduction-for | 59.13 |
| extended-batch-normalization | 78.65 |
| effective-version-space-reduction-for | 58.93 |
| stochastic-optimization-of-plain | 88.08 |
| ica-with-reconstruction-cost-for-efficient | 52.9 |
| selective-unsupervised-feature-learning-with | 61.94 |
| increasing-trustworthiness-of-deep-neural | 88.03 |
| general-e2-equivariant-steerable-cnns-1 | 88.83 |
| convolutional-clustering-for-unsupervised | 74.1 |
| a-framework-for-contrastive-self-supervised | 92.15 |
| effective-version-space-reduction-for | 57.31 |
| receptive-fields-without-spike-triggering | 60.1 |
| how-important-is-weight-symmetry-in | 57.32 |
| revisiting-a-knn-based-image-classification | 99.6 |
| remixmatch-semi-supervised-learning-with-1 | 77.80 |
| effective-version-space-reduction-for | 59.33 |
| generative-pretraining-from-pixels | 94.2 |
| dlme-deep-local-flatness-manifold-embedding | 90.1 |
| vision-models-are-more-robust-and-fair-when | 97.3 |
| discriminative-unsupervised-feature-learning-1 | 72.8 |
| spinalnet-deep-neural-network-with-gradual-1 | 98.66 |
| fixmatch-simplifying-semi-supervised-learning-1 | 73.77 |
| fixmatch-simplifying-semi-supervised-learning-1 | 72.01 |
| scaling-the-scattering-transform-deep-hybrid | 64.6 |
| discriminative-learning-of-sum-product | 62.3 |
| extended-batch-normalization | 75.57 |
| scale-equivariant-steerable-networks-1 | 91.49 |
| remixmatch-semi-supervised-learning-with-1 | 93.82 |
| mixmatch-a-holistic-approach-to-semi | 94.41 |
| toward-understanding-supervised | 98.24 |
| dont-wait-just-weight-improving-unsupervised | 69.15 |
| enaet-self-trained-ensemble-autoencoding | 95.48 |
| dont-wait-just-weight-improving-unsupervised | 63.13 |
| your-diffusion-model-is-secretly-a-zero-shot | 95.4 |
| effective-version-space-reduction-for | 58.84 |
| dont-wait-just-weight-improving-unsupervised | 71.12 |
| improved-regularization-of-convolutional | 87.26 |
| extended-batch-normalization | 72.66 |
| fixmatch-simplifying-semi-supervised-learning-1 | 89.59 |
| dont-wait-just-weight-improving-unsupervised | 68.19 |
| unsupervised-feature-learning-with-c-svddnet | 68.2 |
| general-e2-equivariant-steerable-cnns-1 | 90.20 |
| probabilistic-structural-latent | 78.8 |
| toward-understanding-supervised | 98.36 |
| reversible-architectures-for-arbitrarily-deep | 84.6 |
| image-augmentation-for-object-image | 83.45 |
| extended-batch-normalization | 79.3 |
| a-framework-for-contrastive-self-supervised | 78.36 |
| image-augmentation-for-object-image | 83.47 |
| remixmatch-semi-supervised-learning-with-1 | 89.82 |
| neural-architecture-transfer | 97.2 |
| hybridnet-classification-and-reconstruction | 82.00 |
| general-e2-equivariant-steerable-cnns-1 | 87.26 |
| image-augmentation-for-object-image | 81.45 |
| towards-class-specific-unit | 85.42 |
| fixmatch-simplifying-semi-supervised-learning-1 | 78.57 |
| stacked-what-where-auto-encoders | 74.3 |
| wavemix-lite-a-resource-efficient-neural | 70.88 |
| hybridnet-classification-and-reconstruction | 74.33 |
| remixmatch-semi-supervised-learning-with-1 | 74.30 |
| image-augmentation-for-object-image | 83.00 |
| nsganetv2-evolutionary-multi-objective | 92.0 |
| deep-neural-networks-motivated-by-partial | 78.3 |
| increasing-trustworthiness-of-deep-neural | 71.65 |
| scaling-the-scattering-transform-deep-hybrid | 76.6 |
| spinalnet-deep-neural-network-with-gradual-1 | 95.44 |
| scaling-the-scattering-transform-deep-hybrid | 60.2 |