5th Workshop on Document Analysis and Recognition
Description: An ultra customizable generic theme for Stackbit
Data Efficient Learning with Less Data State of the art AI and Deep Learning are very data hungry. This comes at significant cost including larger resource costs (multiple expensive GPUs and cloud costs), training times (often times multiple days), and human labeling costs and time. Decile attempts to solve this by answering the following question. Can we train state of the art deep models with only a sample (say 5 to 10\%) of massive datasets, while having neglibible impact in accuracy? Can we do this whil
Reduce end to end training time from days to hours and hours to minutes using co resets and d ata s election. CORDS implements a number of state of the art data subset selection algorithms and coreset algorithms. Some of the algorithms currently implemented with CORDS include: GLISTER, GradMatchOMP, GradMatchFixed, CRAIG, SubmodularSelection, RandomSelection etc
DISTIL is a library that features many state-of-the-art active learning algorithms. Implemented in PyTorch, it gives fast and efficient implementations of these active learning algorithms. It allows users to modularly insert active learning selection into their pre-existing training loops with minimal change. Most importantly, it features promising results in achieving high model performance with less amount of labeled data. If you are looking to cut down on labeling costs, DISTIL should be your go-to for g
5th Workshop on Document Analysis and Recognition