# CF-Net: A Cross-Feature Reconstruction Network for High-Accuracy 1-Bit Target Classification

arXiv: https://arxiv.org/abs/2512.15105 · HTML: https://mlsanity.com/paper/2512.15105/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 67 references that are in the map.
- First posted on arXiv 17 Dec 2025.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
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- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Fully-Convolutional Siamese Networks for Object Tracking](https://mlsanity.com/paper/1606.09549/) (2016)
- [Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning](https://mlsanity.com/paper/2212.14532/) (2022)
- [Logit Standardization in Knowledge Distillation](https://mlsanity.com/paper/2403.01427/) (2024)
- [Multimodal Fusion Transformer for Remote Sensing Image Classification](https://mlsanity.com/paper/2203.16952/) (2022)
- [RadarDistill: Boosting Radar-based Object Detection Performance via Knowledge Distillation from LiDAR Features](https://mlsanity.com/paper/2403.05061/) (2024)
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- [CubeLearn: End-to-end Learning for Human Motion Recognition from Raw mmWave Radar Signals](https://mlsanity.com/paper/2111.03976/) (2021)
- [Efficient Image Pre-Training with Siamese Cropped Masked Autoencoders](https://mlsanity.com/paper/2403.17823/) (2024)
- [SFFNet: A Wavelet-Based Spatial and Frequency Domain Fusion Network for Remote Sensing Segmentation](https://mlsanity.com/paper/2405.01992/) (2024)
- [Siamese Learning with Joint Alignment and Regression for Weakly-Supervised Video Paragraph Grounding](https://mlsanity.com/paper/2403.11463/) (2024)
- [Transformer-based Federated Learning for Multi-Label Remote Sensing Image Classification](https://mlsanity.com/paper/2405.15405/) (2024)
- [CD-CTFM: A Lightweight CNN-Transformer Network for Remote Sensing Cloud Detection Fusing Multiscale Features](https://mlsanity.com/paper/2306.07186/) (2023)
- [One-Bit Target Detection in Collocated MIMO Radar and Performance Degradation Analysis](https://mlsanity.com/paper/2012.10780/) (2020)
- [One-Bit Spectrum Sensing for Cognitive Radio](https://mlsanity.com/paper/2306.13558/) (2023)

## Abstract (arXiv)

Target classification is a fundamental task in radar systems, and its performance critically depends on the quantization precision of the signal. While high-precision quantization (e.g. 16-bit) is well established, 1-bit quantization offers distinct advantages by enabling direct sampling at high frequencies and eliminating complex intermediate stages. However, its extreme quantization leads to significant information loss. Although higher sampling rates can compensate for this loss, such oversampling is impractical at the high frequencies targeted for direct sampling. To achieve high-accuracy classification directly from 1-bit radar data under the same sampling rate, this paper proposes a novel two-stage deep learning framework, CF-Net. First, we introduce a self-supervised pre-training strategy based on a dual-branch U-Net architecture. This network learns to restore high-fidelity 16-bit images from their 1-bit counterparts via a cross-feature reconstruction task, forcing the 1-bit encoder to learn robust features despite extreme quantization. Subsequently, this pre-trained encoder is repurposed and fine-tuned for the downstream multi-class target classification task. Experiments on two radar target datasets demonstrate that CF-Net can effectively extract discriminative features from 1-bit imagery, achieving comparable and even superior accuracy to some 16-bit methods without oversampling.