在 OpenGL 中主要使用 4x4 矩阵来表示转换，这个和 3x4 的相机矩阵不同。然而，OpenGL 中的 GL_PROJECTION 和 GL_MODEL...
这一部分就是knowledge-augmented encoder。 ---- 训练 上面已经描述了预训练阶段和QA finetune阶段的任务。
深度自编码在异常检测中得到了广泛的应用。通过对正常数据的训练，期望自编码器对异常输入产生比正常输入更高的重构误差，以此作为识别异常的判据。然而，这一假设在实践中...
VR.png Augmented reality and virtual reality encounters are changing the manner in which organizations...As opposed to addressing client inquiries regarding an item with straightforward words utilize augmented...Read: "The Impact of Artificial Intelligence on Business" Your augmented-or virtual-reality innovation...Investigate every one of the manners in which augmented and virtual reality advancements might most likely
GCDST: A Graph-based and Copy-augmented Multi-domain Dialogue State Tracking 概要 问题动机 大多数现有的方法都在单个领域上独立训练
= augmentation(**data) ## 数据增强image, mask, whatever_data, additional = augmented["image"], augmented...["mask"], augmented["whatever_data"], augmented["additional"]4....1)])augmented = aug(**annotations)visualize(augmented, category_id_to_name)Resize 数据增强：aug = get_aug(...([CenterCrop(p=1, height=224, width=224)])augmented = aug(**annotations)print(augmented['category_id'...= aug(image=image, mask=mask)image_rot90 = augmented['image']mask_rot90 = augmented['mask']visualize
mask=mask) image_padded = augmented['image'] mask_padded = augmented['mask'] print(image_padded.shape...= aug(image=image_padded, mask=mask_padded) image_cropped = augmented['image'] mask_cropped = augmented...= augmented['image'] mask_h_flipped = augmented['mask'] visualize(image_h_flipped, mask_h_flipped,...= augmented['image'] mask_v_flipped = augmented['mask'] visualize(image_v_flipped, mask_v_flipped,...image_transposed = augmented['image'] mask_transposed = augmented['mask'] visualize(image_transposed
= tokenizer.decode(output_ids, skip_special_tokens=True) print("Augmented Text->",augmented_text)...= unmasker(new_mask_sent) augmented_text = augmented_text_list[0]['sequence'] print("Augmented text...->",augmented_text) #I went to see a new movie in the theater 我们可以看到对于输入文本“I went to see a movie in...= orig_word: augmented_text = res['sequence'] break print("Augmented text->",augmented_text...= gpt_output[0]['generated_text'] print("Augmented text->",augmented_text) #I went to see a movie
= near_synonym_augmentation(text) print("近义词增强结果:", augmented_text) 等价词替换（Paraphrase Augmentation）:...= paraphrase_augmentation(text) print("等价词替换结果:", augmented_text) 回译（Back Translation）: 回译是一种用于数据增强的方法...= random_deletion(text) augmented_text_swap = random_swap(text) augmented_text_insertion = random_insertion...(text) print("随机删除结果:", augmented_text_deletion) print("随机交换结果:", augmented_text_swap) print("随机插入结果...:", augmented_text_insertion) 示例演示了如何应用不同的NLP数据增强方法。
" # Keyboard aug = nac.KeyboardAug() augmented_text = aug.augment(text) print(augmented_text)..." # OCR aug = nac.OcrAug() augmented_text = aug.augment(text) print(augmented_text) 3、Random Augmenter..." # Synonym aug = naw.SynonymAug(aug_src='wordnet') augmented_text = aug.augment(text) print(augmented_text..." # Back translation aug = naw.BackTranslationAug() augmented_text = aug.augment(text) print(augmented_text...', tokenizer=_tokenizer) augmented_text = aug.augment(text) print(augmented_text) 段句级增强 文本扩充也可以在句子层面进行
Building Agents with Imagination https://github.com/createamind/Imagination-Augmented-Agents Intelligent.... [1] This tutorial presents a new family of approaches for imagination-based planning: Imagination-Augmented...Imagination Augmented Agent [in progress] The I2A learns to combine information from its model-free and...imagination-augmented paths....[imagination-augmented agent.ipynb] More materials on model based + model free RL The Predictron: End-To-End
这项合作使礼来能够部署Yseop的世界级企业自动化平台Augmented Analyst，以加速将礼来的药品带给患者。...根据协议，礼来将利用Yseop的Augmented Analyst将数据转化为高质量的叙述和监管提交报告，并实现规模化和无误差。除了提高质量，Yseop还使用户能够将时间集中在更有影响力的活动上。...很荣幸有礼来站在我们这边，将我们的Augmented Analyst平台推向生命科学领域的新高度，这最终将使整个生命科学界受益。"...其行业领先的Augmented Analyst NLP人工智能平台支持企业用户的无代码应用。...Augmented Analyst平台分析企业数据，并提供洞察力和文件自动化，赋予员工权力，支持其Augmented Financial Analyst和Augmented Medical Writer
cvtutorials.png") image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) transformed_1 = A.HorizontalFlip(p=) augmented_image..._1 = transformed_1(image=image)['image'] transformed_2 = A.ShiftScaleRotate(p=) augmented_image_2 =...transformed_2(image=image)['image'] augmented_images = [image, augmented_image_1, augmented_image_2]...plt.figure(figsize=(, )) for i in range(, ): plt.subplot(,,i) plt.imshow(augmented_images[i
:")ia.imshow(image_aug)Augmented:?...ia.seed(4)image_aug = rotate(image=image)print("Re-Augmented:")ia.imshow(image_aug)Re-Augmented:?...ia.seed(5)image_aug = rotate(image=image)print("Augmented:")ia.imshow(image_aug)Augmented:?...batch：")ia.imshow(np.hstack(images_aug))Augmented batch：?...:")ia.imshow(np.hstack(images_aug))Augmented:?
Apple is working on at least two AR projects that include an augmented reality headset set to be released...in late 2022 or 2023 followed by a sleeker pair of augmented reality glasses coming at a later date....coming when, but it's now clear that an AR/VR (or mixed reality) headset will be released, followed by augmented...The headset will focus on VR with some limited AR capabilities, but Apple has deeper augmented reality...Augmented reality doesn't hinge on immersive content and while less exciting because it's augmenting
A.Compose([ A.RandomRotate90(), A.HorizontalFlip(), A.RandomBrightnessContrast(), ]) augmented_image...iaa.Sometimes(0.5, iaa.GaussianBlur(sigma=(0, 2.0))), iaa.ContrastNormalization((0.5, 2.0)), ]) augmented_image...下面是如何使用它: from textattack.augmentation import WordNetAugmenter augmenter = WordNetAugmenter() augmented_text...from taae import SynonymAugmenter augmenter = SynonymAugmenter() augmented_text = augmenter.augment...augmenter = A.Compose([ A.PitchShift(), A.TimeStretch(), A.AddBackgroundNoise(), ]) augmented_audio
请看一个特意设计的例子： def show_arguments(base, extended=None, improved=None, augmented=None): print("base..."extended is", extended) if improved is not None: print("improved is", improved) if augmented...is not None: print("augmented is", augmented) 当阅读调用该函数的代码时，有时很难理解发生了什么： show_arguments("hello...相反，你可以将这些参数标记为仅限关键字： def show_arguments(base, *, extended=None, improved=None, augmented=None): print...is not None: print("augmented is", augmented) 现在，你不能用位置参数传入额外的参数： show_arguments("hello", "extra
= ""text_augmented_prompt += "You are an expert web developer who specializes in HTML and CSS....\\n"text_augmented_prompt += "The text elements are:\\n" + texts + "\\n"text_augmented_prompt += "You...\\n"text_augmented_prompt += "Include all CSS code in the HTML file itself....\\n"text_augmented_prompt += "Do not hallucinate any dependencies to external files....文本增强提示法（Text Augmented Prompting）: 这种方法在直接提示法的基础上增加了从网页中提取的所有文本信息。
), A.Blur(blur_limit=3), A.OpticalDistortion(), A.GridDistortion(), ]) random.seed(100) augmented_image...= transform(image=image)['image'] visualize(augmented_image) 上述代码会对原始图像做出7种一定概率的随机变换，然后生成一种图像，如： 现在...A.Blur(blur_limit=3), A.OpticalDistortion(), A.GridDistortion(), ]) augmented_image...= transform(image=image)['image'] image = cv2.cvtColor(augmented_image, cv2.COLOR_RGB2BGR) cv2...= transform(image=image)['image'] image = cv2.cvtColor(augmented_image, cv2.COLOR_RGB2BGR)
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