We lifted 9 functions out of this paper's own repositories and ran 8 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| ncclab-sustech/cocog-2 | canonical | 8 of 9 |
| Function | Status | Where it lives |
|---|---|---|
| default_loader | Ran | ncclab-sustech/cocog-2/dataset.py pointer only (licence: NONE) · get_code("ac269a0e4b8d946e") |
| get_loss_dim | Ran | ncclab-sustech/cocog-2/guidance_set.py pointer only (licence: NONE) · get_code("6bf70eea0a13600e") |
| get_loss_similarity | Ran | ncclab-sustech/cocog-2/guidance_set.py pointer only (licence: NONE) · get_code("6ad77f4e176c0a30") |
| get_loss_smooth | Ran | ncclab-sustech/cocog-2/guidance_set.py pointer only (licence: NONE) · get_code("6aa2e91fb7ed939e") |
| is_image_file | Ran | ncclab-sustech/cocog-2/dataset.py pointer only (licence: NONE) · get_code("0ae9b5b39db5b9c0") |
| make_dataset | Ran | ncclab-sustech/cocog-2/dataset.py pointer only (licence: NONE) · get_code("a3f4286c53c29d0a") |
| trunc_normal_ | Ran | ncclab-sustech/cocog-2/prior_networks.py pointer only (licence: NONE) · get_code("02566da69866c48c") |
| unpatchify | Ran | ncclab-sustech/cocog-2/prior_networks.py pointer only (licence: NONE) · get_code("4f6768a3bc645e2a") |
| encode_image | Not yet run | ncclab-sustech/cocog-2/customized_pipe.py pointer only (licence: NONE) · get_code("62f91e0069da2565") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Humans interpret complex visual stimuli using abstract concepts that facilitate decision-making tasks such as food selection and risk avoidance. Similarity judgment tasks are effective for exploring these concepts. However, methods for controllable image generation in concept space are underdeveloped. In this study, we present a novel framework called CoCoG-2, which integrates generated visual stimuli into similarity judgment tasks. CoCoG-2 utilizes a training-free guidance algorithm to enhance generation flexibility. CoCoG-2 framework is versatile for creating experimental stimuli based on human concepts, supporting various strategies for guiding visual stimuli generation, and demonstrating how these stimuli can validate various experimental hypotheses. CoCoG-2 will advance our understanding of the causal relationship between concept representations and behaviors by generating visual stimuli. The code is available at \url{https://github.com/ncclab-sustech/CoCoG-2}.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2407.14949")
get_code_for_paper("2407.14949")
have("2407.14949")
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