SuperLoss: A Generic Loss for Robust Curriculum Learning
Authors: Thibault Castells, Philippe Weinzaepfel, Jerome Revaud
NeurIPS 2020 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 3 Experimental results |
| Researcher Affiliation | Industry | Thibault Castells Naver Labs Europe EMAIL Philippe Weinzaepfel Naver Labs Europe EMAIL Jerome Revaud Naver Labs Europe EMAIL |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper refers to third-party open-source projects (detectron2, cnnimageretrieval-pytorch) used in their experiments, but does not provide specific access to their own implementation code for the Super Loss method. |
| Open Datasets | Yes | We perform a toy regression experiment on MNIST [26]... We experiment on the larger UTKFace dataset [70]... CIFAR-10 and CIFAR-100 [24]... Web Vision [31] is a large-scale dataset... We perform experiments for the object detection task on Pascal VOC [7]... We evaluate the Super Loss on the image retrieval task using the Revisited Oxford and Paris benchmark [42]. To train our method, we use the large-scale Landmarks dataset [2]... We also experiment with the cleaned dataset [12]... |
| Dataset Splits | Yes | Landmarks dataset [2] that is composed of about 200K images (divided into 160K/40K for training/validation) |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper mentions software frameworks like detectron2 and cnnimageretrieval-pytorch, but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | Our protocol is to first train the baseline and tune its hyper-parameters (e.g., learning rate, weight decay, etc.)... We set the regularization parameter to λ = 1 for CIFAR-10 and to λ = 0.25 for CIFAR-100... train a Res Net-18 model using SGD for 120 epochs with a weight decay of 10-4, an initial learning rate of 0.1, divided by 10 at 30, 60 and 90 epochs. |