An **ECA-Net** is a type of convolutional neural network that utilises an [Efficient Channel Attention](https://paperswithcode.com/method/efficient-channel-attention) module.

Model to adapt:
We use Ultra Fast Structure-aware Deep Lane Detection (UFLD) as baseline and strictly adopt its training scheme and hyperparameters. UFLD treats lane detection as a row-based classification problem and utilizes the row anchors defined by TuSimple.

Unsupervised Domain Adaptation with SGPCS:
SGPCS builds upon PCS  and performs in-domain contrastive learning and crossdomain self-supervised learning via cluster prototypes. 

We reformulate the pseudo label selection mechanism from SGADA. For each lane, we select the highest confidence value from the griding cells of each row anchor. Based on their griding cell position, the confidence values are divided into two cases: absent lane points and present lane points. Thereby, the last griding cell represents absent lane points as in. For each case, we calculate the mean confidence over the corresponding lanes. We then use the thresholds defined by SGADA to decide whether the prediction is treated as a pseudo label.

Our overall objective function comprises the in-domain and cross-domain loss from PCS, the losses defined by UFLD, and our adopted pseudo loss from SGADA. We adjust the momentum for memory bank feature updates to 0.5 and use spherical K-means with K = 2,500 to cluster them into prototypes.

SGPCS

CARLANE: A Lane Detection Benchmark for Unsupervised Domain Adaptation from Simulation to multiple Real-World Domains

ECA-Net

ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks

Distributed training has become a pervasive and effective approach for training a large neural network
(NN) model with processing massive data. However, it is very challenging to satisfy requirements
from various NN models, diverse computing resources, and their dynamic changes during a training
job. In this study, we design our distributed training framework in a systematic end-to-end view to
provide the built-in adaptive ability for different scenarios, especially for industrial applications and
production environments, by fully considering resource allocation, model partition, task placement,
and distributed execution. Based on the unified distributed graph and the unified cluster object,
our adaptive framework is equipped with a global cost model and a global planner, which can
enable arbitrary parallelism, resource-aware placement, multi-mode execution, fault-tolerant, and
elastic distributed training. The experiments demonstrate that our framework can satisfy various
requirements from the diversity of applications and the heterogeneity of resources with highly
competitive performance.

Source	ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com