Similarity of Pre-trained and Fine-tuned Representations

In transfer learning, only the last part of the networks - the so-called head - is often fine-tuned. Representation similarity analysis shows that the most significant change still occurs in the head even if all weights are updatable. However, recent results from few-shot learning have shown that representation change in the early layers, which are mostly convolutional, is beneficial, especially in the case of cross-domain adaption. In our paper, we find out whether that also holds true for transfer learning. In addition, we analyze the change of representation in transfer learning, both during pre-training and fine-tuning, and find out that pre-trained structure is unlearned if not usable.Comment: Workshop of Updatable Machine Learning at ICML 202

NAPC: A Neural Algorithm for Automated Passenger Counting in Public Transport on a Privacy-Friendly Dataset

Real-time load information in public transport is of high importance for both passengers and service providers. Neural algorithms have shown a high performance on various object counting tasks and play a continually growing methodological role in developing automated passenger counting systems. However, the publication of public-space video footage is often contradicted by legal and ethical considerations to protect the passengers’ privacy. This work proposes an end-to-end Long Short-Term Memory network with a problem-adapted cost function that learned to count boarding and alighting passengers on a publicly available, comprehensive dataset of approx. 13,000 manually annotated low-resolution 3D LiDAR video recordings (depth information only) from the doorways of a regional train. These depth recordings do not allow the identification of single individuals. For each door opening phase, the trained models predict the correct passenger count (ranging from 0 to 67) in approx. 96% of boarding and alighting, respectively. Repeated training with different training and validation sets confirms the independence of this result from a specific test set.DFG, 414044773, Open Access Publizieren 2021 - 2022 / Technische Universität Berli

Berlin-APC: A Privacy-Friendly Dataset for Automated Passenger Counting in Public Transport

This document provides a short technical introduction to the Berlin-APC dataset. The dataset consists of two files, a HDF5 file which contains the image sequences, and a CSV file which contains the labels. The CSV file has three columns: (1) the image sequence name; (2) the number of boarding passengers in that image sequence; (3) the number of alighting passengers in that image sequence. The image sequence names also serve as keys in the HDF5 file. The HDF5 file’s datasets (indexed by the aforementioned sequence names) are float16 arrays of the shape (number of frames, 20, 25), the pixel values range between 0–1, and the frame rate is 10 frames per second