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5.8 | 9.8 | |
7 months ago | 7 days ago | |
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MIT License | Apache License 2.0 |
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[D] Problems with proprietary datasets
Now is it possible that some of these images were a part of train set of these models ? Maybe, but we can't really be sure without having access to the original dataset. To this end, are there any works that study this phenomenon more deeply and technically (with metrics etc.) ? I know few attempts to reproduce DALL-E and CLIP on open datasets but not sure whether such studies have been performed. Unfortunately I lack both the resources as well as technical competency to perform such studies myself but would love to see if you folks know anything about this.
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What are some alternatives?
DALLE2-pytorch - Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch
whisper-timestamped - Multilingual Automatic Speech Recognition with word-level timestamps and confidence
CoCa-pytorch - Implementation of CoCa, Contrastive Captioners are Image-Text Foundation Models, in Pytorch
petals - 🌸 Run LLMs at home, BitTorrent-style. Fine-tuning and inference up to 10x faster than offloading
CapDec - CapDec: SOTA Zero Shot Image Captioning Using CLIP and GPT2, EMNLP 2022 (findings)
LOGICGUIDE - Plug in and Play implementation of "Certified Reasoning with Language Models" that elevates model reasoning by 40%
VehicleFinder-CTIM
speechbrain - A PyTorch-based Speech Toolkit
DALLE-pytorch - Implementation / replication of DALL-E, OpenAI's Text to Image Transformer, in Pytorch
Chinese-CLIP - Chinese version of CLIP which achieves Chinese cross-modal retrieval and representation generation.
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.