Contains codes corresponding to the paper Adaptive Sample Selection for Robust Learning under Label Noise which corresponds to Ch-3 of my master's thesis.

Contains codes corresponding to the paper Memorization in Deep Neural Networks: Does the Loss Function Matter? which corresponds to Ch-4 of my master's thesis.

• Create a file for the conda environment details [conda_env_details.txt)]
• Create a file containing hyperparameter details for all the experiments [hyperparam_details.md] [desired format]
• Upload all the baseline codes in BARE folder
• Upload codes for my algorithms
• Upload codes pertaining to experiments on memorization and overparameterization
• [] Modularize your codes (along the lines of this/this PyTorch-based repository)

• To be upated.

• Generalized Cross-Entropy (NeurIPS'18) [paper]
• Symmetric Cross Entropy Loss (ICCV'19) [paper]

• Forward Loss Correction [paper]

• Dimensionality Driven Learning (ICML'18) [paper]
• Meta-Ren (ICML'18) [paper] [official code] [unofficial code] [unofficial code]
• Unsupervised Label Noise Modelling & Loss Correction [paper]
• Meta Net (NeurIPS'19) [paper] [official code]
• DivideMix (ICLR'20) [paper] [official code]
• JoCoR (CVPR'20) [paper] [official code]

• Joint Optimization (CVPR'18) [paper]