[CVPR 2024] Diversity-aware Channel Pruning for StyleGAN Compression
🖥 Github: https://github.com/jiwoogit/dcp-gan
📕 Paper: https://arxiv.org/pdf/2403.13548v1.pdf
⭐️ Tasks: https://paperswithcode.com/task/image-generation
🔥Dataset: https://paperswithcode.com/dataset/ffhq
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🖥 Github: https://github.com/jiwoogit/dcp-gan
📕 Paper: https://arxiv.org/pdf/2403.13548v1.pdf
⭐️ Tasks: https://paperswithcode.com/task/image-generation
🔥Dataset: https://paperswithcode.com/dataset/ffhq
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🔥Grok-1 LLM .
Apache 2.0
▪ Model: https://dagshub.com/xai/grok-1
▪ Page: https://x.ai/blog/grok-os
▪ Code: https://github.com/xai-org/grok-1
▪ Hugging face:https://huggingface.co/xai-org/grok-1
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Apache 2.0
▪ Model: https://dagshub.com/xai/grok-1
▪ Page: https://x.ai/blog/grok-os
▪ Code: https://github.com/xai-org/grok-1
▪ Hugging face:https://huggingface.co/xai-org/grok-1
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❤3🔥2
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🦾 Supervision: reusable computer vision tools
▪Github:
▪Project
▪Colab
▪Supervision Cookbooks
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pip install supervision▪Github:
▪Project
▪Colab
▪Supervision Cookbooks
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👍8❤4🔥1
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🖼 One-Step Image Translation with Text-to-Image Models
CycleGAN-Turbo
▪Paper: https://arxiv.org/abs/2403.12036
▪Code: https://github.com/GaParmar/img2img-turbo
▪Demo: http://huggingface.co/spaces/gparmar/img2img-turbo-sketch
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CycleGAN-Turbo
▪Paper: https://arxiv.org/abs/2403.12036
▪Code: https://github.com/GaParmar/img2img-turbo
▪Demo: http://huggingface.co/spaces/gparmar/img2img-turbo-sketch
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H-SAM
🖥 Github: https://github.com/cccccczh404/h-sam
📕 Paper: https://arxiv.org/pdf/2403.18271v1.pdf
🔥Dataset: https://paperswithcode.com/dataset/promise12
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🖥 Github: https://github.com/cccccczh404/h-sam
📕 Paper: https://arxiv.org/pdf/2403.18271v1.pdf
🔥Dataset: https://paperswithcode.com/dataset/promise12
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📌skscope: Fast Sparse-Constraint Optimization
🖥 Github: https://github.com/abess-team/skscope
📕 Paper: https://arxiv.org/abs/2403.18540v1
🔥Dataset: skscope.readthedocs.io
Topics
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🖥 Github: https://github.com/abess-team/skscope
📕 Paper: https://arxiv.org/abs/2403.18540v1
🔥Dataset: skscope.readthedocs.io
Topics
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🪴 SceneScript, a novel method for reconstructing environments and representing the layout of physical spaces
▪Paper
▪Project
▪Dataset
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▪Paper
▪Project
▪Dataset
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Long-Form Factuality in Large Language Models
🖥 Github: https://github.com/google-deepmind/long-form-factuality
📕 Paper: https://arxiv.org/pdf/2403.18802v1.pdf
🔥Dataset: https://paperswithcode.com/dataset/truthfulqa
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🖥 Github: https://github.com/google-deepmind/long-form-factuality
📕 Paper: https://arxiv.org/pdf/2403.18802v1.pdf
🔥Dataset: https://paperswithcode.com/dataset/truthfulqa
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⚡ StreamMultiDiffusion: Real-Time Interactive Generation with Region-Based Semantic Control
▪Сode: https://github.com/ironjr/StreamMultiDiffusion
▪Paper: https://arxiv.org/abs/2403.09055
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▪Сode: https://github.com/ironjr/StreamMultiDiffusion
▪Paper: https://arxiv.org/abs/2403.09055
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🧬 Evolving New Foundation Models: Unleashing the Power of Automating Model Development
▪Blog: https://sakana.ai/evolutionary-model-merge/
▪Paper: https://arxiv.org/abs/2403.13187
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▪Blog: https://sakana.ai/evolutionary-model-merge/
▪Paper: https://arxiv.org/abs/2403.13187
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AutoWebGLM: Bootstrap And Reinforce A Large Language Model-based Web Navigating Agent
🖥 Github: https://github.com/thudm/autowebglm
📕 Paper: https://arxiv.org/abs/2404.03648v1
🔥Dataset: https://paperswithcode.com/dataset/mind2web
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🖥 Github: https://github.com/thudm/autowebglm
📕 Paper: https://arxiv.org/abs/2404.03648v1
🔥Dataset: https://paperswithcode.com/dataset/mind2web
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Mixtral 8x22B weights are now available
📦model: https://dagshub.com/MistralAI/Mixtral-8x22B-v0.1
🌐page: https://mistral.ai
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📦model: https://dagshub.com/MistralAI/Mixtral-8x22B-v0.1
🌐page: https://mistral.ai
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👍6
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🌊 LaVague: automate automation with Large Action Model framework
▪Github: https://github.com/lavague-ai/LaVague
▪Docs: https://docs.lavague.ai/en/latest/docs/
▪Colab: https://colab.research.google.com/github/lavague-ai/LaVague/blob/main/docs/docs/get-
started/quick-tour.ipynb
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▪Github: https://github.com/lavague-ai/LaVague
▪Docs: https://docs.lavague.ai/en/latest/docs/
▪Colab: https://colab.research.google.com/github/lavague-ai/LaVague/blob/main/docs/docs/get-
started/quick-tour.ipynb
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