Paper

Neural Architecture Search for Generative Adversarial Networks: A Comprehensive Review and Critical Analysis

Unreadunread

AI summary

arXiv:2606. 26169v1 Announce Type: new Abstract: Neural Architecture Search (NAS) has emerged as a pivotal technique in optimizing the design of Generative Adversarial Networks (GANs), automating the search for effective architectures while addressing the challenges inherent in manual design.

Reading status

Priority

About this paper

Authors: Abrar Alotaibi, Moataz Ahmed
Released: 2026-06-26 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because Neural Architecture Search for Generative Adversarial Networks: A Comprehensive Review and Critical Analysis overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", 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)". Matching terms: text, eval, rate, contexts. Source: arxiv cs.LG (Machine Learning).
arXiv: 2606.26169

Ask Claude about this paper

Posts a comment with @claude so the runner spins up a Claude Code session scoped to Neural Architecture Search for Generative Adversarial Networks: A Comprehensive Review and Critical Analysis. Replies appear in the conversation below.

Comments

No comments yet.

Related records

No links yet.