Feature Compression
19 papers with code • 0 benchmarks • 0 datasets
Compress data for machine interpretability to perform downstream tasks, rather than for human perception.
Benchmarks
These leaderboards are used to track progress in Feature Compression
Most implemented papers
Supervised Feature Compression based on Counterfactual Analysis
Counterfactual Explanations are becoming a de-facto standard in post-hoc interpretable machine learning.
Efficient Feature Compression for Edge-Cloud Systems
Optimizing computation in an edge-cloud system is an important yet challenging problem.
SGCN: Exploiting Compressed-Sparse Features in Deep Graph Convolutional Network Accelerators
A GCN takes as input an arbitrarily structured graph and executes a series of layers which exploit the graph's structure to calculate their output features.
FrankenSplit: Efficient Neural Feature Compression with Shallow Variational Bottleneck Injection for Mobile Edge Computing
The rise of mobile AI accelerators allows latency-sensitive applications to execute lightweight Deep Neural Networks (DNNs) on the client side.
Understanding Deep Representation Learning via Layerwise Feature Compression and Discrimination
To the best of our knowledge, this is the first quantitative characterization of feature evolution in hierarchical representations of deep linear networks.
WidthFormer: Toward Efficient Transformer-based BEV View Transformation
In this work, we present WidthFormer, a novel transformer-based Bird's-Eye-View (BEV) 3D detection method tailored for real-time autonomous-driving applications.
Learning to Manipulate Artistic Images
Recent advancement in computer vision has significantly lowered the barriers to artistic creation.
Effective Communication with Dynamic Feature Compression
The remote wireless control of industrial systems is one of the major use cases for 5G and beyond systems: in these cases, the massive amounts of sensory information that need to be shared over the wireless medium may overload even high-capacity connections.
EMIFF: Enhanced Multi-scale Image Feature Fusion for Vehicle-Infrastructure Cooperative 3D Object Detection
In autonomous driving, cooperative perception makes use of multi-view cameras from both vehicles and infrastructure, providing a global vantage point with rich semantic context of road conditions beyond a single vehicle viewpoint.
FOOL: Addressing the Downlink Bottleneck in Satellite Computing with Neural Feature Compression
Further, it embeds context and leverages inter-tile dependencies to lower transfer costs with negligible overhead.