Paper

TriHead-GAN: A Generative Adversarial Network with Triple-Head Discriminator for Carbon Emission Time Series Generation

Unreadunread

AI summary

arXiv:2606. 07569v1 Announce Type: new Abstract: Accurate carbon emission monitoring is critical for climate policy and emerging regulatory mechanisms such as the EU Carbon Border Adjustment Mechanism, yet city-level high-frequency monitoring data remain extremely scarce, severely limiting data-hungry deep learning models.

Reading status

Priority

About this paper

Authors: Zesen Wang, Lijuan Lan, Yonggang Li, Chunhua Yang
Released: 2026-06-09 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because TriHead-GAN: A Generative Adversarial Network with Triple-Head Discriminator for Carbon Emission Time Series Generation overlaps with clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "Only continuous soft prefixes hit both EM axes at once on Qwen-2.5-7B-Instruct: discrete prompt searches split between the alignment objective and the distributional objective, and both discretizations of the soft prefix collapse (MODERATE confidence)", clean result "Training one persona to emit a [ZLT] marker without bystanders adopting it has a one-cell-wide LR x epochs window on Qwen2.5-7B-Instruct (LOW confidence)". Matching terms: under, distributional, source, line, rate, emit, factor, leakage. Source: arxiv cs.LG (Machine Learning).
arXiv: 2606.07569

Ask Claude about this paper

Posts a comment with @claude so the runner spins up a Claude Code session scoped to TriHead-GAN: A Generative Adversarial Network with Triple-Head Discriminator for Carbon Emission Time Series Generation. Replies appear in the conversation below.

Comments

No comments yet.

Related records

No links yet.