Loading the SOTA2 catalog…
SOTA2 Research · papers
Find papers, implementations, and the benchmark evidence behind state-of-the-art AI systems.
| 2021 |
| arxiv 2108.00831 |
| Instance Similarity Learning for Unsupervised Feature Representation | Ziwei Wang, Yunsong Wang, Ziyi Wu | 2021 | arxiv 2108.02721 |
|---|
| m-RevNet: Deep Reversible Neural Networks with Momentum | Duo Li, Shang-Hua Gao | 2021 | arxiv 2108.05862 |
|---|
| Access Control Using Spatially Invariant Permutation of Feature Maps for Semantic Segmentation Models | Hiroki Ito, MaungMaung AprilPyone, Hitoshi Kiya | 2021 | arxiv 2109.01332 |
|---|
| Automated Seed Quality Testing System using GAN & Active Learning | Sandeep Nagar, Prateek Pani, Raj Nair | 2021 | arxiv 2110.00777 |
|---|
| Combining Human Predictions with Model Probabilities via Confusion Matrices and Calibration | Gavin Kerrigan, Padhraic Smyth, Mark Steyvers | 2021 | arxiv 2109.14591 |
|---|
| ParticleAugment: Sampling-Based Data Augmentation | Alexander Tsaregorodtsev, Vasileios Belagiannis | 2021 | arxiv 2106.08693 |
|---|
| TESDA: Transform Enabled Statistical Detection of Attacks in Deep Neural Networks | Chandramouli Amarnath, Aishwarya H. Balwani, Kwondo Ma | 2021 | arxiv 2110.08447 |
|---|
| Combating Noise: Semi-supervised Learning by Region Uncertainty Quantification | Zhenyu Wang, Yali Li, Ye Guo | 2021 | arxiv 2111.00928 |
|---|
| Sign-MAML: Efficient Model-Agnostic Meta-Learning by SignSGD | Chen Fan, Parikshit Ram, Sijia Liu | 2021 | arxiv 2109.07497 |
|---|
| Defending Against Image Corruptions Through Adversarial Augmentations | Dan A. Calian, Florian Stimberg, Olivia Wiles | 2021 | arxiv 2104.01086 |
|---|