Unverified50% confidenceFactExact time
Inception V1 is a classic convolutional network proposed by Google in 2014 and, due to its breakthrough on ImageNet and its compact structure, has long been the "fruit fly" model for interpretability research; its conv5-1 layer has 480 channels and was systematically studied by Olah et al. using feature visualization.
1
Sources
50%
Confidence
Long-term
Relevance
10/22/2026
First Seen
Sources
Was the Neural Network Always "Clean"? Interpreting Goodfire's Long-Form Explainability Essay
youtubeXiaoyu Li10/16/2026
Related Entities
Related Claims
UnverifiedInceptionV1 (GoogLeNet) is an early deep convolutional network proposed by Google in 2014, whose Inception module improves feature extraction efficiency by using convolutional kernels of different sizes in parallel.78% similarUnverifiedThe Google Cloud-related integration switched to v1 version endpoints in v1.16.0.63% similarUnverifiedGoogle Cloud's N1/N2 series instances support nested virtualization but require manual enabling.61% similarUnverifiedGoogle Cloud leverages its proprietary Gemini series models and Vertex AI platform to hold technical advantages in retrieval-augmented generation (RAG) and multimodal understanding.61% similarUnverifiedGoogle's Imagen and OpenAI's DALL-E 3 adopt a similar cascaded diffusion generation technical approach.61% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1085041API
curl https://kongchang.com/api/v1/knowledge/claims/1085041MCP
get_claim(id=1085041)