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Found 6 papers, 2 papers with code
On Pitfalls of Measuring Occlusion Robustness through Data Distortion
Over the past years, the crucial role of data has largely been shadowed by the field's focus on architectures and training procedures.
Generalisation and the Risk--Entropy Curve
In this paper we show that the expected generalisation performance of a learning machine is determined by the distribution of risks or equivalently its logarithm -- a quantity we term the risk entropy -- and the fluctuations in a quantity we call the training ratio.
On the Effects of Artificial Data Modification
Data distortion is commonly applied in vision models during both training (e. g methods like MixUp and CutMix) and evaluation (e. g. shape-texture bias and robustness).
FMix: Enhancing Mixed Sample Data Augmentation
Finally, we show that a consequence of the difference between interpolating MSDA such as MixUp and masking MSDA such as FMix is that the two can be combined to improve performance even further.
Ranked #3 on
Image Classification
on Fashion-MNIST
Rethinking Generalisation
In this paper, a new approach to computing the generalisation performance is presented that assumes the distribution of risks, $\rho(r)$, for a learning scenario is known.
