Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth | 0 | 0.34 | 2021 |
Generalised Lipschitz Regularisation Equals Distributional Robustness | 0 | 0.34 | 2021 |
Teaching with Commentaries. | 0 | 0.34 | 2021 |
Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth. | 0 | 0.34 | 2021 |
MIST: Multiple Instance Spatial Transformer | 0 | 0.34 | 2021 |
Big Self-Supervised Models are Strong Semi-Supervised Learners | 0 | 0.34 | 2020 |
A Simple Framework for Contrastive Learning of Visual Representations | 2 | 0.36 | 2020 |
Revisiting Spatial Invariance with Low-Rank Local Connectivity | 0 | 0.34 | 2020 |
The Origins and Prevalence of Texture Bias in Convolutional Neural Networks | 0 | 0.34 | 2020 |
Similarity of Neural Network Representations Revisited. | 2 | 0.36 | 2019 |
Saccader: Improving Accuracy of Hard Attention Models for Vision | 0 | 0.34 | 2019 |
When Does Label Smoothing Help? | 7 | 0.40 | 2019 |
Cerberus: A Multi-headed Derenderer. | 0 | 0.34 | 2019 |
Lipschitz Networks and Distributional Robustness. | 0 | 0.34 | 2018 |
Domain Adaptive Transfer Learning with Specialist Models. | 3 | 0.38 | 2018 |
Do Better Imagenet Models Transfer Better? | 37 | 1.31 | 2018 |