Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
SuperLoss: A Generic Loss for Robust Curriculum Learning
Authors: Thibault Castells, Philippe Weinzaepfel, Jerome Revaud
NeurIPS 2020 | Venue PDF | 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 ο¬rst train the baseline and tune its hyper-parameters (e.g., learning rate, weight decay, etc.)... We set the regularization parameter to Ξ» = 1 for CIFAR-10 and to Ξ» = 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. |